commit e797f6f31e9f7508864c7c3e92281e47def457c0
Author: ModelHub XC <noreply@modelhub.org.cn>
Date:   Sat Apr 11 05:28:57 2026 +0800

    初始化项目，由ModelHub XC社区提供模型
    
    Model: hlyn/prompt-injection-judge-3b
    Source: Original Platform

diff --git a/.gitattributes b/.gitattributes
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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+checkpoint-50/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+checkpoint-63/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+judge-q4_k_m.gguf filter=lfs diff=lfs merge=lfs -text
+judge-dolphin3-3b-f16.gguf filter=lfs diff=lfs merge=lfs -text
diff --git a/README.md b/README.md
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--- /dev/null
+++ b/README.md
@@ -0,0 +1,81 @@
+---
+library_name: peft
+license: llama3.2
+base_model: dphn/Dolphin3.0-Llama3.2-3B
+tags:
+- axolotl
+- base_model:adapter:dphn/Dolphin3.0-Llama3.2-3B
+- lora
+- dora
+- security
+- prompt-injection
+- transformers
+datasets:
+- karan11/defender-judge-fine-tune
+pipeline_tag: text-generation
+---
+
+# Defender Security Judge — Dolphin 3.0 Llama 3.2 3B
+
+A fine-tuned, production-hardened **prompt injection security judge** built on top of [dphn/Dolphin3.0-Llama3.2-3B](https://huggingface.co/dphn/Dolphin3.0-Llama3.2-3B).
+
+This model is Stage 2 of the **Defender** multi-layer LLM security pipeline — a real-time adversarial firewall that intercepts, analyzes, and classifies user prompts before they ever reach a protected LLM.
+
+---
+
+## Benchmark Results
+
+Evaluated against the **[rogue-security/prompt-injections-benchmark](https://huggingface.co/datasets/rogue-security/prompt-injections-benchmark)** — the industry-standard Qualifire benchmark used to evaluate production prompt injection defenses.
+
+| Metric | Score |
+|---|---|
+| **Accuracy** | **90.00%** |
+| **F1 Score** | **0.9038** |
+| **Precision** | 88.68% |
+| **Recall** | **92.16%** |
+
+> A 3B quantized model running entirely offline achieving 90% accuracy on the hardest curated jailbreak benchmark available. No API calls. No latency. No cost.
+
+---
+
+## What Makes This Model Different
+
+**Zero refusals.** Built on the uncensored Dolphin base, it coldly analyzes any attack — no matter how explicit — without flinching or refusing to process the payload.
+
+**Rigid JSON output.** DoRA fine-tuning permanently hardwires the model to emit only structured `{"decision", "confidence", "reason", "allowed_payload"}` JSON. No preamble. No yapping.
+
+**Calibrated confidence.** Trained with Gaussian confidence noise on ambiguous samples, the model's `confidence` field is mathematically trustworthy — not the overconfident `0.99` you get from vanilla LLMs.
+
+**Long-context immunity.** Trained at `sequence_len: 8192` with 98.37% sample packing efficiency. The model can read an 8,000-token document and catch an attack buried at token 7,500.
+
+---
+
+## Training Details
+
+- **Technique:** DoRA (Weight-Decomposed LoRA) + NEFTune (α=5.0) + Flash Attention + Sample Packing
+- **Hardware:** NVIDIA H100 80GB SXM5
+- **Training Time:** ~14 minutes
+- **Loss:** 2.30 → 0.18 (converged cleanly across 3 epochs)
+- **Dataset:** [`karan11/defender-judge-fine-tune`](https://huggingface.co/datasets/karan11/defender-judge-fine-tune) — 2,700 DeBERTa-scored, calibration-hardened samples
+
+---
+
+## Available Artifacts
+
+| File | Description |
+|---|---|
+| `adapter_model.safetensors` | Raw LoRA adapter weights |
+| `judge-dolphin3-3b-f16.gguf` | Full merged model in F16 (6.4 GB) |
+| `judge-q4_k_m.gguf` | **Production artifact** — Q4_K_M quantized (2.0 GB) |
+
+---
+
+## Intended Use
+
+This model is **strictly a security classifier**. It is not a general-purpose assistant.  
+Load it with `llama-cpp-python` and pass it the Defender system prompt for correct behavior.
+
+```python
+from llama_cpp import Llama
+
+llm = Llama(model_path="judge-q4_k_m.gguf", n_gpu_layers=-1, n_ctx=8192)
diff --git a/adapter_config.json b/adapter_config.json
new file mode 100644
index 0000000..bbab826
--- /dev/null
+++ b/adapter_config.json
@@ -0,0 +1,42 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "dphn/Dolphin3.0-Llama3.2-3B",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": null,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "k_proj",
+    "down_proj",
+    "v_proj",
+    "up_proj",
+    "gate_proj",
+    "o_proj"
+  ],
+  "target_parameters": [],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}
\ No newline at end of file
diff --git a/adapter_model.safetensors b/adapter_model.safetensors
new file mode 100644
index 0000000..964c61a
--- /dev/null
+++ b/adapter_model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8e4504c7e7ed9ae5ee3453117b027ad3951485505712648810bad778493b95a6
+size 194563400
diff --git a/chat_template.jinja b/chat_template.jinja
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index 0000000..37fa038
--- /dev/null
+++ b/chat_template.jinja
@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Dolphin, created by Eric Hartford. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Dolphin, created by Eric Hartford. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}
diff --git a/checkpoint-50/README.md b/checkpoint-50/README.md
new file mode 100644
index 0000000..a59545a
--- /dev/null
+++ b/checkpoint-50/README.md
@@ -0,0 +1,208 @@
+---
+base_model: dphn/Dolphin3.0-Llama3.2-3B
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- axolotl
+- base_model:adapter:dphn/Dolphin3.0-Llama3.2-3B
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.17.1
\ No newline at end of file
diff --git a/checkpoint-50/adapter_config.json b/checkpoint-50/adapter_config.json
new file mode 100644
index 0000000..bbab826
--- /dev/null
+++ b/checkpoint-50/adapter_config.json
@@ -0,0 +1,42 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "dphn/Dolphin3.0-Llama3.2-3B",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": null,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "k_proj",
+    "down_proj",
+    "v_proj",
+    "up_proj",
+    "gate_proj",
+    "o_proj"
+  ],
+  "target_parameters": [],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}
\ No newline at end of file
diff --git a/checkpoint-50/adapter_model.safetensors b/checkpoint-50/adapter_model.safetensors
new file mode 100644
index 0000000..a51e637
--- /dev/null
+++ b/checkpoint-50/adapter_model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d146b84905a73e20346796c64407623c8520ab21b570d4373e240721a10a7c4
+size 194563400
diff --git a/checkpoint-50/chat_template.jinja b/checkpoint-50/chat_template.jinja
new file mode 100644
index 0000000..37fa038
--- /dev/null
+++ b/checkpoint-50/chat_template.jinja
@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Dolphin, created by Eric Hartford. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Dolphin, created by Eric Hartford. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}
diff --git a/checkpoint-50/optimizer.pt b/checkpoint-50/optimizer.pt
new file mode 100644
index 0000000..bf54f64
--- /dev/null
+++ b/checkpoint-50/optimizer.pt
@@ -0,0 +1,3 @@
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+oid sha256:fa5e9b3b8ae8ce75e4bc2f15da50b41a51fe6024bdcc8743ee14e3dc25d74901
+size 99236229
diff --git a/checkpoint-50/rng_state.pth b/checkpoint-50/rng_state.pth
new file mode 100644
index 0000000..470f7c9
--- /dev/null
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c4d0bf15ed2d9bd0722831a061ddd72b425a4042e542f9cd68afee09da160c22
+size 14645
diff --git a/checkpoint-50/scheduler.pt b/checkpoint-50/scheduler.pt
new file mode 100644
index 0000000..0227ffc
--- /dev/null
+++ b/checkpoint-50/scheduler.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d288b2d12f4dcfd8baffc646eff0066fa3401560c79741973ba3588874bc2516
+size 1465
diff --git a/checkpoint-50/special_tokens_map.json b/checkpoint-50/special_tokens_map.json
new file mode 100644
index 0000000..44e8cb8
--- /dev/null
+++ b/checkpoint-50/special_tokens_map.json
@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
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diff --git a/checkpoint-63/README.md b/checkpoint-63/README.md
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+---
+base_model: dphn/Dolphin3.0-Llama3.2-3B
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- axolotl
+- base_model:adapter:dphn/Dolphin3.0-Llama3.2-3B
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.17.1
\ No newline at end of file
diff --git a/checkpoint-63/adapter_config.json b/checkpoint-63/adapter_config.json
new file mode 100644
index 0000000..bbab826
--- /dev/null
+++ b/checkpoint-63/adapter_config.json
@@ -0,0 +1,42 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "dphn/Dolphin3.0-Llama3.2-3B",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": null,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "k_proj",
+    "down_proj",
+    "v_proj",
+    "up_proj",
+    "gate_proj",
+    "o_proj"
+  ],
+  "target_parameters": [],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}
\ No newline at end of file
diff --git a/checkpoint-63/adapter_model.safetensors b/checkpoint-63/adapter_model.safetensors
new file mode 100644
index 0000000..964c61a
--- /dev/null
+++ b/checkpoint-63/adapter_model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8e4504c7e7ed9ae5ee3453117b027ad3951485505712648810bad778493b95a6
+size 194563400
diff --git a/checkpoint-63/chat_template.jinja b/checkpoint-63/chat_template.jinja
new file mode 100644
index 0000000..37fa038
--- /dev/null
+++ b/checkpoint-63/chat_template.jinja
@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Dolphin, created by Eric Hartford. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Dolphin, created by Eric Hartford. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}
diff --git a/checkpoint-63/optimizer.pt b/checkpoint-63/optimizer.pt
new file mode 100644
index 0000000..6299ffa
--- /dev/null
+++ b/checkpoint-63/optimizer.pt
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+version https://git-lfs.github.com/spec/v1
+oid sha256:2dccd5f8e86c94244b70e33a69846b01afb0048925160b1733f5f0bf274e367e
+size 99236229
diff --git a/checkpoint-63/rng_state.pth b/checkpoint-63/rng_state.pth
new file mode 100644
index 0000000..95dc4ce
--- /dev/null
+++ b/checkpoint-63/rng_state.pth
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+size 14645
diff --git a/checkpoint-63/scheduler.pt b/checkpoint-63/scheduler.pt
new file mode 100644
index 0000000..193b1c0
--- /dev/null
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+size 1465
diff --git a/checkpoint-63/special_tokens_map.json b/checkpoint-63/special_tokens_map.json
new file mode 100644
index 0000000..44e8cb8
--- /dev/null
+++ b/checkpoint-63/special_tokens_map.json
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+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
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+  }
+}
diff --git a/checkpoint-63/tokenizer.json b/checkpoint-63/tokenizer.json
new file mode 100644
index 0000000..cf79b3a
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new file mode 100644
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--- /dev/null
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+      "content": "<|reserved_special_token_246|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128255": {
+      "content": "<|reserved_special_token_247|>",
+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "128256": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "128257": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|im_end|>",
+  "extra_special_tokens": {},
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|end_of_text|>",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}
diff --git a/checkpoint-63/trainer_state.json b/checkpoint-63/trainer_state.json
new file mode 100644
index 0000000..788fd79
--- /dev/null
+++ b/checkpoint-63/trainer_state.json
@@ -0,0 +1,166 @@
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+      "tokens_per_second_per_gpu": 730.88
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+      "memory/max_allocated (GiB)": 57.73,
+      "step": 60,
+      "tokens_per_second_per_gpu": 723.26
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+  ],
+  "logging_steps": 5,
+  "max_steps": 63,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 3,
+  "save_steps": 50,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": true
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+      "attributes": {}
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+  "total_flos": 1.3854128835290726e+17,
+  "train_batch_size": 4,
+  "trial_name": null,
+  "trial_params": null
+}
diff --git a/checkpoint-63/training_args.bin b/checkpoint-63/training_args.bin
new file mode 100644
index 0000000..452b6b4
--- /dev/null
+++ b/checkpoint-63/training_args.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:777cf783ce2107ca224701957ecefd6409bd4496cec7b943c91914b5e4aacc22
+size 9809
diff --git a/config.json b/config.json
new file mode 100644
index 0000000..3a0fef5
--- /dev/null
+++ b/config.json
@@ -0,0 +1,50 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": 128256,
+  "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,
+  "pretraining_tp": 1,
+  "quantization_config": {
+    "_load_in_4bit": false,
+    "_load_in_8bit": true,
+    "bnb_4bit_compute_dtype": "float32",
+    "bnb_4bit_quant_storage": "uint8",
+    "bnb_4bit_quant_type": "fp4",
+    "bnb_4bit_use_double_quant": false,
+    "llm_int8_enable_fp32_cpu_offload": false,
+    "llm_int8_has_fp16_weight": false,
+    "llm_int8_skip_modules": null,
+    "llm_int8_threshold": 6.0,
+    "load_in_4bit": false,
+    "load_in_8bit": true,
+    "quant_method": "bitsandbytes"
+  },
+  "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,
+  "transformers_version": "4.57.0",
+  "use_cache": false,
+  "vocab_size": 128258
+}
diff --git a/debug.log b/debug.log
new file mode 100644
index 0000000..04af68f
--- /dev/null
+++ b/debug.log
@@ -0,0 +1,286 @@
+[2026-03-29 17:06:48,176] [DEBUG] [axolotl.utils.config.resolve_dtype:66] [PID:1870] bf16 support detected, enabling for this configuration.
+[2026-03-29 17:06:48,413] [DEBUG] [axolotl.utils.config.log_gpu_memory_usage:127] [PID:1870] baseline 0.000GB ()
+[2026-03-29 17:06:48,417] [INFO] [axolotl.cli.config.load_cfg:248] [PID:1870] config:
+{
+  "activation_offloading": false,
+  "adapter": "lora",
+  "axolotl_config_path": "config.yaml",
+  "base_model": "dphn/Dolphin3.0-Llama3.2-3B",
+  "base_model_config": "dphn/Dolphin3.0-Llama3.2-3B",
+  "batch_size": 16,
+  "bf16": true,
+  "capabilities": {
+    "bf16": true,
+    "compute_capability": "sm_90",
+    "fp8": false,
+    "n_gpu": 1,
+    "n_node": 1
+  },
+  "chat_template": "tokenizer_default",
+  "context_parallel_size": 1,
+  "dataloader_num_workers": 1,
+  "dataloader_pin_memory": true,
+  "dataloader_prefetch_factor": 256,
+  "dataset_processes": 26,
+  "datasets": [
+    {
+      "chat_template": "tokenizer_default",
+      "data_files": [
+        "train.jsonl"
+      ],
+      "field_messages": "messages",
+      "message_property_mappings": {
+        "content": "content",
+        "role": "role"
+      },
+      "path": "karan11/defender-judge-fine-tune",
+      "trust_remote_code": false,
+      "type": "chat_template"
+    }
+  ],
+  "ddp": false,
+  "device": "cuda:0",
+  "dion_rank_fraction": 1.0,
+  "dion_rank_multiple_of": 1,
+  "env_capabilities": {
+    "torch_version": "2.7.1"
+  },
+  "eval_batch_size": 4,
+  "eval_causal_lm_metrics": [
+    "sacrebleu",
+    "comet",
+    "ter",
+    "chrf"
+  ],
+  "eval_max_new_tokens": 128,
+  "eval_sample_packing": false,
+  "eval_steps": 50,
+  "eval_table_size": 0,
+  "experimental_skip_move_to_device": true,
+  "flash_attention": true,
+  "fp16": false,
+  "gradient_accumulation_steps": 4,
+  "gradient_checkpointing": true,
+  "gradient_checkpointing_kwargs": {
+    "use_reentrant": true
+  },
+  "include_tkps": true,
+  "is_llama_derived_model": true,
+  "learning_rate": 0.00015,
+  "lisa_layers_attribute": "model.layers",
+  "load_best_model_at_end": false,
+  "load_in_4bit": false,
+  "load_in_8bit": true,
+  "local_rank": 0,
+  "logging_steps": 5,
+  "lora_alpha": 64,
+  "lora_dropout": 0.1,
+  "lora_r": 32,
+  "lora_target_modules": [
+    "q_proj",
+    "k_proj",
+    "v_proj",
+    "o_proj",
+    "gate_proj",
+    "up_proj",
+    "down_proj"
+  ],
+  "loraplus_lr_embedding": 1e-06,
+  "lr_scheduler": "cosine",
+  "max_grad_norm": 1.0,
+  "mean_resizing_embeddings": false,
+  "micro_batch_size": 4,
+  "model_config_type": "llama",
+  "neftune_noise_alpha": 5.0,
+  "num_epochs": 3.0,
+  "optimizer": "adamw_bnb_8bit",
+  "output_dir": "./outputs/defender-judge",
+  "pad_to_sequence_len": true,
+  "pretrain_multipack_attn": true,
+  "profiler_steps_start": 0,
+  "qlora_sharded_model_loading": false,
+  "ray_num_workers": 1,
+  "remove_unused_columns": false,
+  "resources_per_worker": {
+    "GPU": 1
+  },
+  "sample_packing": true,
+  "sample_packing_bin_size": 200,
+  "sample_packing_group_size": 100000,
+  "save_only_model": false,
+  "save_safetensors": true,
+  "save_steps": 50,
+  "sequence_len": 8192,
+  "shuffle_before_merging_datasets": false,
+  "shuffle_merged_datasets": true,
+  "skip_prepare_dataset": false,
+  "streaming_multipack_buffer_size": 10000,
+  "strict": false,
+  "tensor_parallel_size": 1,
+  "tiled_mlp_use_original_mlp": true,
+  "tokenizer_config": "dphn/Dolphin3.0-Llama3.2-3B",
+  "tokenizer_save_jinja_files": true,
+  "torch_dtype": "torch.bfloat16",
+  "train_on_inputs": false,
+  "trl": {
+    "log_completions": false,
+    "mask_truncated_completions": false,
+    "ref_model_mixup_alpha": 0.9,
+    "ref_model_sync_steps": 64,
+    "scale_rewards": true,
+    "sync_ref_model": false,
+    "use_vllm": false,
+    "vllm_server_host": "0.0.0.0",
+    "vllm_server_port": 8000
+  },
+  "use_ray": false,
+  "val_set_size": 0.0,
+  "vllm": {
+    "device": "auto",
+    "dtype": "auto",
+    "gpu_memory_utilization": 0.9,
+    "host": "0.0.0.0",
+    "port": 8000
+  },
+  "warmup_ratio": 0.1,
+  "weight_decay": 0.05,
+  "world_size": 1
+}
+[2026-03-29 17:06:48,431] [WARNING] [axolotl.cli.checks.check_user_token:46] [PID:1870] Error verifying HuggingFace token. Remember to log in using `huggingface-cli login` and get your access token from https://huggingface.co/settings/tokens if you want to use gated models or datasets.
+[2026-03-29 17:06:49,360] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:278] [PID:1870] EOS: 128256 / <|im_end|>
+[2026-03-29 17:06:49,362] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:279] [PID:1870] BOS: 128000 / <|begin_of_text|>
+[2026-03-29 17:06:49,364] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:280] [PID:1870] PAD: 128001 / <|end_of_text|>
+[2026-03-29 17:06:49,365] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:281] [PID:1870] UNK: None / None
+[2026-03-29 17:06:49,368] [INFO] [axolotl.utils.data.shared.load_preprocessed_dataset:476] [PID:1870] Unable to find prepared dataset in last_run_prepared/b72df10b48d5f00efef65cbe761c72f7
+[2026-03-29 17:06:49,370] [INFO] [axolotl.utils.data.sft._load_raw_datasets:320] [PID:1870] Loading raw datasets...
+[2026-03-29 17:06:49,372] [WARNING] [axolotl.utils.data.sft._load_raw_datasets:322] [PID:1870] Processing datasets during training can lead to VRAM instability. Please pre-process your dataset using `axolotl preprocess path/to/config.yml`.
+[2026-03-29 17:06:50,431] [INFO] [axolotl.utils.data.wrappers.get_dataset_wrapper:87] [PID:1870] Loading dataset: karan11/defender-judge-fine-tune with base_type: chat_template and prompt_style: None
+[2026-03-29 17:06:50,440] [INFO] [axolotl.prompt_strategies.chat_template.__call__:969] [PID:1870] Using chat template:
+---
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Dolphin, created by Eric Hartford. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Dolphin, created by Eric Hartford. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}
+
+---
+
Tokenizing Prompts (num_proc=26):   0%|                                                                                                                  | 0/2700 [00:00<?, ? examples/s]
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Tokenizing Prompts (num_proc=26):  19%|███████████████████▊                                                                                   | 520/2700 [00:01<00:05, 411.19 examples/s]
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Tokenizing Prompts (num_proc=26): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 2700/2700 [00:05<00:00, 510.82 examples/s]
+[2026-03-29 17:06:56,225] [INFO] [axolotl.utils.data.utils.handle_long_seq_in_dataset:218] [PID:1870] min_input_len: 915
+[2026-03-29 17:06:56,227] [INFO] [axolotl.utils.data.utils.handle_long_seq_in_dataset:220] [PID:1870] max_input_len: 5341
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Dropping Long Sequences (>8192) (num_proc=26):  39%|█████████████████████████████████▉                                                      | 1040/2700 [00:01<00:01, 1190.54 examples/s]
Dropping Long Sequences (>8192) (num_proc=26): 100%|████████████████████████████████████████████████████████████████████████████████████████| 2700/2700 [00:01<00:00, 1481.80 examples/s]
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Drop Samples with Zero Trainable Tokens (num_proc=26):   4%|███▏                                                                               | 104/2700 [00:01<00:36, 71.14 examples/s]
Drop Samples with Zero Trainable Tokens (num_proc=26):  39%|███████████████████████████████▏                                                 | 1040/2700 [00:01<00:01, 881.31 examples/s]
Drop Samples with Zero Trainable Tokens (num_proc=26):  58%|██████████████████████████████████████████████▏                                 | 1559/2700 [00:01<00:01, 1117.53 examples/s]
Drop Samples with Zero Trainable Tokens (num_proc=26): 100%|████████████████████████████████████████████████████████████████████████████████| 2700/2700 [00:02<00:00, 1205.04 examples/s]
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Add position_id column (Sample Packing) (num_proc=26):  58%|██████████████████████████████████████████████▏                                 | 1560/2700 [00:00<00:00, 3137.08 examples/s]
Add position_id column (Sample Packing) (num_proc=26): 100%|████████████████████████████████████████████████████████████████████████████████| 2700/2700 [00:00<00:00, 5046.91 examples/s]
Add position_id column (Sample Packing) (num_proc=26): 100%|████████████████████████████████████████████████████████████████████████████████| 2700/2700 [00:01<00:00, 2033.93 examples/s]
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Saving the dataset (0/10 shards):   0%|                                                                                                                  | 0/2700 [00:00<?, ? examples/s]
Saving the dataset (0/10 shards):  10%|██████████▏                                                                                           | 270/2700 [00:00<00:01, 1392.03 examples/s]
Saving the dataset (1/10 shards):  10%|██████████▏                                                                                           | 270/2700 [00:00<00:01, 1392.03 examples/s]
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Saving the dataset (4/10 shards):  40%|████████████████████████████████████████▍                                                            | 1080/2700 [00:00<00:01, 1392.03 examples/s]
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Saving the dataset (6/10 shards):  70%|██████████████████████████████████████████████████████████████████████▋                              | 1890/2700 [00:00<00:00, 1392.03 examples/s]
Saving the dataset (7/10 shards):  70%|██████████████████████████████████████████████████████████████████████▋                              | 1890/2700 [00:00<00:00, 1392.03 examples/s]
Saving the dataset (8/10 shards):  80%|████████████████████████████████████████████████████████████████████████████████▊                    | 2160/2700 [00:00<00:00, 1392.03 examples/s]
Saving the dataset (9/10 shards):  90%|██████████████████████████████████████████████████████████████████████████████████████████▉          | 2430/2700 [00:00<00:00, 1392.03 examples/s]
Saving the dataset (10/10 shards): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 2700/2700 [00:00<00:00, 1392.03 examples/s]
Saving the dataset (10/10 shards): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 2700/2700 [00:00<00:00, 8593.97 examples/s]
+[2026-03-29 17:07:02,957] [DEBUG] [axolotl.utils.trainer.calculate_total_num_steps:404] [PID:1870] total_num_tokens: 2_740_122
+[2026-03-29 17:07:03,005] [DEBUG] [axolotl.utils.trainer.calculate_total_num_steps:422] [PID:1870] `total_supervised_tokens: 202_860`
+[2026-03-29 17:07:04,075] [DEBUG] [axolotl.utils.samplers.multipack.__len__:458] [PID:1870] generate_batches time: 0.48715806007385254
+[2026-03-29 17:07:04,570] [DEBUG] [axolotl.utils.samplers.multipack.__len__:458] [PID:1870] generate_batches time: 0.4939417839050293
+[2026-03-29 17:07:05,063] [DEBUG] [axolotl.utils.samplers.multipack.__len__:458] [PID:1870] generate_batches time: 0.4897637367248535
+[2026-03-29 17:07:05,552] [DEBUG] [axolotl.utils.samplers.multipack.__len__:458] [PID:1870] generate_batches time: 0.4878394603729248
+[2026-03-29 17:07:05,581] [INFO] [axolotl.utils.samplers.multipack.calc_min_len:434] [PID:1870] gather_len_batches: [85]
+[2026-03-29 17:07:05,582] [DEBUG] [axolotl.utils.trainer.calculate_total_num_steps:481] [PID:1870] data_loader_len: 21
+[2026-03-29 17:07:05,583] [INFO] [axolotl.utils.trainer.calc_sample_packing_eff_est:497] [PID:1870] sample_packing_eff_est across ranks: [0.9837869083180147]
+[2026-03-29 17:07:05,585] [DEBUG] [axolotl.utils.trainer.calculate_total_num_steps:509] [PID:1870] sample_packing_eff_est: 0.99
+[2026-03-29 17:07:05,586] [DEBUG] [axolotl.utils.trainer.calculate_total_num_steps:520] [PID:1870] total_num_steps: 63
+[2026-03-29 17:07:05,588] [INFO] [axolotl.utils.data.sft._prepare_standard_dataset:121] [PID:1870] Maximum number of steps set at 63
+[2026-03-29 17:07:05,617] [DEBUG] [axolotl.train.setup_model_and_tokenizer:65] [PID:1870] Loading tokenizer... dphn/Dolphin3.0-Llama3.2-3B
+[2026-03-29 17:07:06,638] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:278] [PID:1870] EOS: 128256 / <|im_end|>
+[2026-03-29 17:07:06,639] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:279] [PID:1870] BOS: 128000 / <|begin_of_text|>
+[2026-03-29 17:07:06,641] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:280] [PID:1870] PAD: 128001 / <|end_of_text|>
+[2026-03-29 17:07:06,642] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:281] [PID:1870] UNK: None / None
+[2026-03-29 17:07:06,644] [DEBUG] [axolotl.train.setup_model_and_tokenizer:74] [PID:1870] Loading model
+[2026-03-29 17:07:06,747] [DEBUG] [axolotl.monkeypatch.transformers.trainer_loss_calc.patch_evaluation_loop:87] [PID:1870] Patched Trainer.evaluation_loop with nanmean loss calculation
+[2026-03-29 17:07:06,752] [DEBUG] [axolotl.monkeypatch.transformers.trainer_loss_calc.patch_maybe_log_save_evaluate:138] [PID:1870] Patched Trainer._maybe_log_save_evaluate with nanmean loss calculation
+[2026-03-29 17:07:06,755] [INFO] [axolotl.loaders.patch_manager._apply_multipack_patches:301] [PID:1870] Applying multipack dataloader patch for sample packing...
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model-00001-of-00002.safetensors:  69%|███████████████████████████████████████████████████████████████████████████▉                                  | 3.43G/4.97G [00:53<00:13, 116MB/s]
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model-00001-of-00002.safetensors:  72%|██████████████████████████████████████████████████████████████████████████████▉                               | 3.56G/4.97G [00:54<00:09, 146MB/s]
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model-00001-of-00002.safetensors:  74%|█████████████████████████████████████████████████████████████████████████████████▉                            | 3.70G/4.97G [00:54<00:07, 165MB/s]
model-00001-of-00002.safetensors:  76%|███████████████████████████████████████████████████████████████████████████████████▍                          | 3.77G/4.97G [00:55<00:07, 152MB/s]
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model-00001-of-00002.safetensors:  79%|██████████████████████████████████████████████████████████████████████████████████████▍                       | 3.90G/4.97G [00:56<00:08, 121MB/s]
model-00001-of-00002.safetensors:  80%|███████████████████████████████████████████████████████████████████████████████████████▉                      | 3.97G/4.97G [00:57<00:09, 106MB/s]
model-00001-of-00002.safetensors:  81%|████████████████████████████████████████████████████████████████████████████████████████▌                    | 4.03G/4.97G [00:59<00:12, 72.6MB/s]
model-00001-of-00002.safetensors:  83%|█████████████████████████████████████████████████████████████████████████████████████████▉                   | 4.10G/4.97G [00:59<00:11, 77.8MB/s]
model-00001-of-00002.safetensors:  84%|███████████████████████████████████████████████████████████████████████████████████████████▍                 | 4.17G/4.97G [01:00<00:08, 91.6MB/s]
model-00001-of-00002.safetensors:  85%|████████████████████████████████████████████████████████████████████████████████████████████▉                | 4.23G/4.97G [01:00<00:07, 97.0MB/s]
model-00001-of-00002.safetensors:  87%|██████████████████████████████████████████████████████████████████████████████████████████████▎              | 4.30G/4.97G [01:01<00:07, 85.1MB/s]
model-00001-of-00002.safetensors:  88%|███████████████████████████████████████████████████████████████████████████████████████████████▊             | 4.37G/4.97G [01:02<00:06, 98.6MB/s]
model-00001-of-00002.safetensors:  89%|█████████████████████████████████████████████████████████████████████████████████████████████████▎           | 4.43G/4.97G [01:03<00:05, 91.5MB/s]
model-00001-of-00002.safetensors:  91%|██████████████████████████████████████████████████████████████████████████████████████████████████▋          | 4.50G/4.97G [01:04<00:06, 72.5MB/s]
model-00001-of-00002.safetensors:  92%|████████████████████████████████████████████████████████████████████████████████████████████████████▏        | 4.56G/4.97G [01:05<00:05, 77.5MB/s]
model-00001-of-00002.safetensors:  93%|█████████████████████████████████████████████████████████████████████████████████████████████████████▋       | 4.63G/4.97G [01:05<00:03, 85.3MB/s]
model-00001-of-00002.safetensors:  95%|███████████████████████████████████████████████████████████████████████████████████████████████████████      | 4.70G/4.97G [01:06<00:03, 86.3MB/s]
model-00001-of-00002.safetensors:  96%|████████████████████████████████████████████████████████████████████████████████████████████████████████▌    | 4.76G/4.97G [01:07<00:02, 96.7MB/s]
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model-00001-of-00002.safetensors:  99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 4.90G/4.97G [01:08<00:00, 92.0MB/s]
model-00001-of-00002.safetensors: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.97G/4.97G [01:09<00:00, 99.9MB/s]
model-00001-of-00002.safetensors: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.97G/4.97G [01:09<00:00, 71.9MB/s]
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model-00002-of-00002.safetensors:   5%|█████                                                                                                        | 67.7M/1.46G [00:05<01:45, 13.2MB/s]
model-00002-of-00002.safetensors:   9%|██████████▏                                                                                                   | 135M/1.46G [00:07<00:56, 23.3MB/s]
model-00002-of-00002.safetensors:  14%|███████████████▏                                                                                              | 202M/1.46G [00:10<00:56, 22.2MB/s]
model-00002-of-00002.safetensors:  18%|████████████████████▎                                                                                         | 269M/1.46G [00:11<00:39, 30.3MB/s]
model-00002-of-00002.safetensors:  23%|█████████████████████████▎                                                                                    | 336M/1.46G [00:11<00:27, 41.4MB/s]
model-00002-of-00002.safetensors:  28%|██████████████████████████████▎                                                                               | 403M/1.46G [00:12<00:21, 48.1MB/s]
model-00002-of-00002.safetensors:  32%|███████████████████████████████████▍                                                                          | 470M/1.46G [00:13<00:17, 55.8MB/s]
model-00002-of-00002.safetensors:  37%|████████████████████████████████████████▍                                                                     | 537M/1.46G [00:14<00:14, 64.2MB/s]
model-00002-of-00002.safetensors:  41%|█████████████████████████████████████████████▌                                                                | 604M/1.46G [00:15<00:12, 66.4MB/s]
model-00002-of-00002.safetensors:  46%|██████████████████████████████████████████████████▌                                                           | 671M/1.46G [00:15<00:10, 73.8MB/s]
model-00002-of-00002.safetensors:  51%|███████████████████████████████████████████████████████▋                                                      | 738M/1.46G [00:16<00:09, 75.1MB/s]
model-00002-of-00002.safetensors:  54%|███████████████████████████████████████████████████████████▍                                                  | 789M/1.46G [00:17<00:09, 71.6MB/s]
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model-00002-of-00002.safetensors:  68%|██████████████████████████████████████████████████████████████████████████▋                                   | 991M/1.46G [00:20<00:05, 83.4MB/s]
model-00002-of-00002.safetensors:  72%|███████████████████████████████████████████████████████████████████████████████▋                              | 1.06G/1.46G [00:20<00:03, 111MB/s]
model-00002-of-00002.safetensors:  77%|███████████████████████████████████████████████████████████████████████████████████▉                         | 1.12G/1.46G [00:21<00:04, 81.5MB/s]
model-00002-of-00002.safetensors:  82%|█████████████████████████████████████████████████████████████████████████████████████████                    | 1.19G/1.46G [00:21<00:02, 92.4MB/s]
model-00002-of-00002.safetensors:  86%|██████████████████████████████████████████████████████████████████████████████████████████████               | 1.26G/1.46G [00:22<00:02, 93.1MB/s]
model-00002-of-00002.safetensors:  91%|███████████████████████████████████████████████████████████████████████████████████████████████████▉          | 1.33G/1.46G [00:23<00:01, 108MB/s]
model-00002-of-00002.safetensors:  95%|████████████████████████████████████████████████████████████████████████████████████████████████████████     | 1.39G/1.46G [00:24<00:00, 90.6MB/s]
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model-00002-of-00002.safetensors: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.46G/1.46G [00:24<00:00, 59.4MB/s]
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+[2026-03-29 17:08:50,906] [INFO] [axolotl.loaders.model._prepare_model_for_quantization:863] [PID:1870] converting PEFT model w/ prepare_model_for_kbit_training
+[2026-03-29 17:08:50,909] [INFO] [axolotl.loaders.model._configure_embedding_dtypes:345] [PID:1870] Converting modules to torch.bfloat16
+[2026-03-29 17:08:50,913] [DEBUG] [axolotl.loaders.model.log_gpu_memory_usage:127] [PID:1870] Memory usage after model load 4.879GB (+4.879GB allocated, +4.984GB reserved)
+trainable params: 48,627,712 || all params: 3,261,383,680 || trainable%: 1.4910
+[2026-03-29 17:08:51,319] [DEBUG] [axolotl.loaders.model.log_gpu_memory_usage:127] [PID:1870] after adapters 3.559GB (+3.559GB allocated, +5.166GB reserved)
+[2026-03-29 17:08:55,931] [INFO] [axolotl.train.save_initial_configs:398] [PID:1870] Pre-saving adapter config to ./outputs/defender-judge...
+[2026-03-29 17:08:55,954] [INFO] [axolotl.train.save_initial_configs:402] [PID:1870] Pre-saving tokenizer to ./outputs/defender-judge...
+[2026-03-29 17:08:56,307] [INFO] [axolotl.train.save_initial_configs:407] [PID:1870] Pre-saving model config to ./outputs/defender-judge...
+[2026-03-29 17:08:56,337] [INFO] [axolotl.train.execute_training:196] [PID:1870] Starting trainer...
+[2026-03-29 17:08:58,996] [DEBUG] [axolotl.utils.samplers.multipack.__len__:458] [PID:1870] generate_batches time: 0.915003776550293
+[2026-03-29 17:08:59,888] [DEBUG] [axolotl.utils.samplers.multipack.__len__:458] [PID:1870] generate_batches time: 0.8903026580810547
+[2026-03-29 17:09:00,762] [DEBUG] [axolotl.utils.samplers.multipack.__len__:458] [PID:1870] generate_batches time: 0.8731865882873535
+[2026-03-29 17:09:01,657] [DEBUG] [axolotl.utils.samplers.multipack.__len__:458] [PID:1870] generate_batches time: 0.8936741352081299
+[2026-03-29 17:09:01,660] [INFO] [axolotl.utils.samplers.multipack.calc_min_len:434] [PID:1870] gather_len_batches: [85]
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+  warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization")
+
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{'loss': 2.3013, 'grad_norm': 1.6521763801574707, 'learning_rate': 9.999999999999999e-05, 'memory/max_active (GiB)': 57.73, 'memory/max_allocated (GiB)': 57.73, 'memory/device_reserved (GiB)': 66.98, 'tokens_per_second_per_gpu': 718.74, 'epoch': 0.24}
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{'loss': 1.3236, 'grad_norm': 1.0924193859100342, 'learning_rate': 0.00014897709775520417, 'memory/max_active (GiB)': 57.73, 'memory/max_allocated (GiB)': 57.73, 'memory/device_reserved (GiB)': 66.98, 'tokens_per_second_per_gpu': 731.56, 'epoch': 0.47}
+
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{'loss': 0.5684, 'grad_norm': 0.6611356735229492, 'learning_rate': 0.00014282678705231832, 'memory/max_active (GiB)': 57.73, 'memory/max_allocated (GiB)': 57.73, 'memory/device_reserved (GiB)': 66.98, 'tokens_per_second_per_gpu': 722.65, 'epoch': 0.71}
+
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{'loss': 0.3071, 'grad_norm': 0.6052656173706055, 'learning_rate': 0.00013155799573326722, 'memory/max_active (GiB)': 57.73, 'memory/max_allocated (GiB)': 57.73, 'memory/device_reserved (GiB)': 66.98, 'tokens_per_second_per_gpu': 728.39, 'epoch': 0.94}
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{'loss': 0.2304, 'grad_norm': 0.5231208801269531, 'learning_rate': 0.00011602111185918203, 'memory/max_active (GiB)': 57.73, 'memory/max_allocated (GiB)': 57.73, 'memory/device_reserved (GiB)': 66.98, 'tokens_per_second_per_gpu': 625.16, 'epoch': 1.14}
+
 40%|███████████████████████████████████████████████████████████████                                                                                                | 25/63 [05:26<07:58, 12.58s/it]
 41%|█████████████████████████████████████████████████████████████████▌                                                                                             | 26/63 [05:39<07:52, 12.77s/it]
 43%|████████████████████████████████████████████████████████████████████▏                                                                                          | 27/63 [05:52<07:43, 12.89s/it]
 44%|██████████████████████████████████████████████████████████████████████▋                                                                                        | 28/63 [06:05<07:33, 12.96s/it]
 46%|█████████████████████████████████████████████████████████████████████████▏                                                                                     | 29/63 [06:18<07:22, 13.02s/it]
 48%|███████████████████████████████████████████████████████████████████████████▋                                                                                   | 30/63 [06:32<07:11, 13.06s/it]
                                                                                                                                                                                                    
{'loss': 0.2163, 'grad_norm': 0.34258556365966797, 'learning_rate': 9.738861082512709e-05, 'memory/max_active (GiB)': 57.73, 'memory/max_allocated (GiB)': 57.73, 'memory/device_reserved (GiB)': 66.98, 'tokens_per_second_per_gpu': 724.72, 'epoch': 1.38}
+
 48%|███████████████████████████████████████████████████████████████████████████▋                                                                                   | 30/63 [06:32<07:11, 13.06s/it]
 49%|██████████████████████████████████████████████████████████████████████████████▏                                                                                | 31/63 [06:45<06:59, 13.11s/it]
 51%|████████████████████████████████████████████████████████████████████████████████▊                                                                              | 32/63 [06:58<06:46, 13.12s/it]
 52%|███████████████████████████████████████████████████████████████████████████████████▎                                                                           | 33/63 [07:11<06:34, 13.14s/it]
 54%|█████████████████████████████████████████████████████████████████████████████████████▊                                                                         | 34/63 [07:24<06:20, 13.13s/it]
 56%|████████████████████████████████████████████████████████████████████████████████████████▎                                                                      | 35/63 [07:37<06:07, 13.14s/it]
                                                                                                                                                                                                    
{'loss': 0.2039, 'grad_norm': 0.3575713038444519, 'learning_rate': 7.706657567761216e-05, 'memory/max_active (GiB)': 57.73, 'memory/max_allocated (GiB)': 57.73, 'memory/device_reserved (GiB)': 66.98, 'tokens_per_second_per_gpu': 723.32, 'epoch': 1.61}
+
 56%|████████████████████████████████████████████████████████████████████████████████████████▎                                                                      | 35/63 [07:37<06:07, 13.14s/it]
 57%|██████████████████████████████████████████████████████████████████████████████████████████▊                                                                    | 36/63 [07:51<05:54, 13.15s/it]
 59%|█████████████████████████████████████████████████████████████████████████████████████████████▍                                                                 | 37/63 [08:04<05:42, 13.16s/it]
 60%|███████████████████████████████████████████████████████████████████████████████████████████████▉                                                               | 38/63 [08:17<05:28, 13.15s/it]
 62%|██████████████████████████████████████████████████████████████████████████████████████████████████▍                                                            | 39/63 [08:30<05:15, 13.16s/it]
 63%|████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                          | 40/63 [08:43<05:02, 13.17s/it]
                                                                                                                                                                                                    
{'loss': 0.1938, 'grad_norm': 0.4452803134918213, 'learning_rate': 5.658858846444006e-05, 'memory/max_active (GiB)': 57.73, 'memory/max_allocated (GiB)': 57.73, 'memory/device_reserved (GiB)': 66.98, 'tokens_per_second_per_gpu': 725.05, 'epoch': 1.85}
+
 63%|████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                          | 40/63 [08:43<05:02, 13.17s/it]
 65%|███████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                       | 41/63 [08:56<04:49, 13.16s/it]
 67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████                                                     | 42/63 [09:10<04:36, 13.16s/it]
 68%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                  | 43/63 [09:23<04:23, 13.16s/it]
 70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                | 44/63 [09:26<03:14, 10.26s/it]
 71%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                             | 45/63 [09:43<03:38, 12.13s/it]
                                                                                                                                                                                                    
{'loss': 0.1849, 'grad_norm': 0.2797813415527344, 'learning_rate': 3.750000000000001e-05, 'memory/max_active (GiB)': 57.73, 'memory/max_allocated (GiB)': 57.73, 'memory/device_reserved (GiB)': 66.98, 'tokens_per_second_per_gpu': 607.58, 'epoch': 2.05}
+
 71%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                             | 45/63 [09:43<03:38, 12.13s/it]
 73%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                           | 46/63 [09:56<03:31, 12.44s/it]
 75%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                        | 47/63 [10:09<03:22, 12.66s/it]
 76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                     | 48/63 [10:22<03:12, 12.80s/it]
 78%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                   | 49/63 [10:35<03:00, 12.92s/it]
 79%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                | 50/63 [10:49<02:48, 12.99s/it]
                                                                                                                                                                                                    
{'loss': 0.1819, 'grad_norm': 0.36559921503067017, 'learning_rate': 2.1241311512366167e-05, 'memory/max_active (GiB)': 57.73, 'memory/max_allocated (GiB)': 57.73, 'memory/device_reserved (GiB)': 66.98, 'tokens_per_second_per_gpu': 730.88, 'epoch': 2.28}
+
 79%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                | 50/63 [10:49<02:48, 12.99s/it][2026-03-29 17:19:50,790] [INFO] [axolotl.core.trainers.base._save:671] [PID:1870] Saving model checkpoint to ./outputs/defender-judge/checkpoint-50
+[2026-03-29 17:19:53,187] [WARNING] [py.warnings._showwarnmsg:110] [PID:1870] /root/miniconda3/envs/py3.11/lib/python3.11/site-packages/bitsandbytes/autograd/_functions.py:186: UserWarning: MatMul8bitLt: inputs will be cast from torch.bfloat16 to float16 during quantization
+  warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization")
+
+
 81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                              | 51/63 [11:04<02:45, 13.77s/it]
 83%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                           | 52/63 [11:17<02:29, 13.59s/it]
 84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                         | 53/63 [11:31<02:14, 13.47s/it]
 86%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                      | 54/63 [11:44<02:00, 13.37s/it]
 87%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                    | 55/63 [11:57<01:46, 13.30s/it]
                                                                                                                                                                                                    
{'loss': 0.1818, 'grad_norm': 0.2546774744987488, 'learning_rate': 9.039468659513327e-06, 'memory/max_active (GiB)': 57.73, 'memory/max_allocated (GiB)': 57.73, 'memory/device_reserved (GiB)': 66.98, 'tokens_per_second_per_gpu': 729.82, 'epoch': 2.52}
+
 87%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                    | 55/63 [11:57<01:46, 13.30s/it]
 89%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                 | 56/63 [12:10<01:32, 13.26s/it]
 90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊               | 57/63 [12:23<01:19, 13.22s/it]
 92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍            | 58/63 [12:36<01:06, 13.21s/it]
 94%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉          | 59/63 [12:49<00:52, 13.19s/it]
 95%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍       | 60/63 [13:03<00:39, 13.18s/it]
                                                                                                                                                                                                    
{'loss': 0.1846, 'grad_norm': 0.27984631061553955, 'learning_rate': 1.8152713029423283e-06, 'memory/max_active (GiB)': 57.73, 'memory/max_allocated (GiB)': 57.73, 'memory/device_reserved (GiB)': 66.98, 'tokens_per_second_per_gpu': 723.26, 'epoch': 2.75}
+
 95%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍       | 60/63 [13:03<00:39, 13.18s/it]
 97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉     | 61/63 [13:16<00:26, 13.17s/it]
 98%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍  | 62/63 [13:29<00:13, 13.17s/it]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [13:42<00:00, 13.17s/it][2026-03-29 17:22:44,281] [INFO] [axolotl.core.trainers.base._save:671] [PID:1870] Saving model checkpoint to ./outputs/defender-judge/checkpoint-63
+
                                                                                                                                                                                                    
{'train_runtime': 825.577, 'train_samples_per_second': 1.221, 'train_steps_per_second': 0.076, 'train_loss': 0.49069616813508293, 'memory/max_active (GiB)': 57.73, 'memory/max_allocated (GiB)': 57.73, 'memory/device_reserved (GiB)': 66.98, 'epoch': 2.89}
+
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [13:45<00:00, 13.17s/it]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [13:45<00:00, 13.10s/it]
+[2026-03-29 17:22:47,727] [INFO] [axolotl.train.save_trained_model:218] [PID:1870] Training completed! Saving trained model to ./outputs/defender-judge.
+[2026-03-29 17:22:48,827] [INFO] [axolotl.train.save_trained_model:336] [PID:1870] Model successfully saved to ./outputs/defender-judge
diff --git a/judge-dolphin3-3b-f16.gguf b/judge-dolphin3-3b-f16.gguf
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diff --git a/judge-q4_k_m.gguf b/judge-q4_k_m.gguf
new file mode 100644
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--- /dev/null
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diff --git a/special_tokens_map.json b/special_tokens_map.json
new file mode 100644
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--- /dev/null
+++ b/special_tokens_map.json
@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
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diff --git a/tokenizer.json b/tokenizer.json
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diff --git a/tokenizer_config.json b/tokenizer_config.json
new file mode 100644
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--- /dev/null
+++ b/tokenizer_config.json
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+{
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