README.md

---
license: mit
datasets:
- zerofata/Instruct-Anime
- zerofata/Roleplay-Anime-Characters
- zerofata/Instruct-Anime-CreativeWriting
- zerofata/Summaries-Anime-FandomPages
base_model:
- zai-org/GLM-4.5-Air
---
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</style>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>Iceblink</title>
  <link rel="preconnect" href="https://fonts.googleapis.com">
  <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
  <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700;800&family=Source+Sans+Pro:ital,wght@0,400;0,600;1,400&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
</head>
<body>

<div class="container">
  <div class="title-container">
    <div class="glitchy-overlay"></div>
    <div class="title-wrapper">
      <h1 class="title-main">
        <span class="title-prefix">ICEBLINK</span>
      </h1>
    </div>
  </div>

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65b19c6c638328850e12d38c/s4Z-P5He2Z3hXD9XtYfwy.png)

  <div class="section-container">
    <div class="section-header">
      <div class="section-indicator"></div>
      <h2 class="section-title">Overview</h2>
    </div>
    <div class="section-content">
      <p>An experimental GLM4.5 Air finetune.</p>
      <p>Had this one in the works for a while, but was struggling to find the right hyperparams to get this model to behave nicely. Thank you to TheDrummer for helping me out with them.</p>
      <p>This model is a creative writing and RP model. It's pretty verbose. The intent is to keep the behavior of the original model, but to slightly improve writing, dialogue & creativity.</p>
    </div>
  </div>

  <div class="section-container">
    <div class="section-header">
      <div class="section-indicator"></div>
      <h2 class="section-title">SillyTavern Settings</h2>
    </div>
    <div class="section-content">
      <h3 class="subheading">Recommended Roleplay Format</h3>
      <div class="data-box">
        <div class="data-row">
            <span class="data-arrow">></span>
            <span class="data-label">Actions:</span>
            <span>In plaintext</span>
        </div>
      <div class="data-row">
            <span class="data-arrow">></span>
            <span class="data-label">Dialogue:</span>
            <span>"In quotes"</span>
      </div>
      <div class="data-row">
            <span class="data-arrow">></span>
            <span class="data-label">Thoughts:</span>
            <span>*In asterisks*</span>
      </div>
      </div>
      <h3 class="subheading">Recommended Samplers</h3>
      <div class="data-box">
        <div class="data-row">
          <span class="data-arrow">></span>
          <span class="data-label">Temp:</span>
          <span>0.8</span>
        </div>
        <div class="data-row">
          <span class="data-arrow">></span>
          <span class="data-label">MinP:</span>
          <span>0.05</span>
        </div>
        <div class="data-row">
          <span class="data-arrow">></span>
          <span class="data-label">TopP:</span>
          <span>0.95</span>
        </div>
      </div>
      <h3 class="subheading">Instruct</h3>
      <div class="data-box">
        <p style="margin: 0;">GLM4.5 (no thinking): <a href="https://huggingface.co/zerofata/GLM-4.5-Iceblink-106B-A12B/raw/main/GLM45-NoThink-SillyTavern-Preset.json">SillyTavern Preset</a></p>
      </div>
    </div>
  </div>

  <div class="section-container">
    <div class="section-header">
      <div class="section-indicator"></div>
      <h2 class="section-title">Quantizations</h2>
    </div>
    <div class="section-content">
      <div style="margin-bottom: 20px;">
        <h3 class="subheading">GGUF</h3>
        <div class="data-box">
          <div class="data-row">
            <span class="data-arrow">></span>
            <a href="https://huggingface.co/bartowski/zerofata_GLM-4.5-Iceblink-106B-A12B-GGUF">iMatrix (bartowski)</a>
          </div>
        </div>
      </div>
    </div>
  </div>

  <div class="section-container">
    <div class="section-header">
      <div class="section-indicator"></div>
      <h2 class="section-title">Creation Process</h2>
    </div>
    <div class="section-content">
      <p>Creation Process: SFT</p>
      <p>SFT on approx 10 million tokens, SFW / NSFW RP, stories, creative instruct & chat data.</p>
      <p>MoE are brutal to train even with a small dataset like mine, so I took a different approach from usual. I used a very low LR in an effort to avoid having to apply DPO / KTO training afterwards.</p>
      <p>I think there's likely a better config to be found, but experimentation with the model to find it is quite draining.</p>
        <div class="dropdown-container">
          <details>
            <summary class="dropdown-summary">
              <span class="dropdown-arrow">></span>
              Axolotl configs
            </summary>
          <div class="dropdown-content">
            <p>Not optimized for cost / performance efficiency, YMMV.</p>
            <div class="config-title">SFT (4*H200)</div>
            <pre><code>base_model: zai-org/GLM-4.5-Air
eot_tokens:
  - "&lt;|user|&gt;"
  - "&lt;|endoftext|&gt;"
special_tokens:
  eos_token: "&lt;|user|&gt;"
<br>
&#35; ====================
&#35; DATASET CONFIGURATION
&#35; ====================
datasets:
  - path: ./data/dataset.jsonl
    type: chat_template
    split: train
    field_messages: messages
    message_property_mappings:
      role: role
      content: content
    roles:
      user: ["user"]
      assistant: ["assistant"]
      system: ["system"]
<br>
dataset_prepared_path: ./last_run_prepared
train_on_inputs: false  &#35; Only train on assistant responses
eval_sample_packing: False
<br>
&#35; ====================
&#35; QLORA CONFIGURATION
&#35; ====================
adapter: qlora
load_in_4bit: true
lora_r: 32
lora_alpha: 32
lora_dropout: 0.1
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj
&#35; lora_modules_to_save:  &#35; Uncomment only if you added NEW tokens
<br>
&#35; ====================
&#35; TRAINING PARAMETERS
&#35; ====================
num_epochs: 3
micro_batch_size: 2
gradient_accumulation_steps: 4
learning_rate: 4.5e-6
optimizer: paged_adamw_8bit
lr_scheduler: rex
warmup_ratio: 0.05
weight_decay: 0.01
max_grad_norm: 1.0
val_set_size: 0.02
<br>
&#35; ====================
&#35; SEQUENCE &amp; PACKING
&#35; ====================
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
<br>
&#35; ====================
&#35; HARDWARE OPTIMIZATIONS
&#35; ====================
bf16: auto
flash_attention: true
gradient_checkpointing: true
<br>
plugins:
  - axolotl.integrations.liger.LigerPlugin
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: false
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true
cut_cross_entropy: false
<br>
deepspeed: deepspeed_configs/zero1.json
<br>
&#35; ====================
&#35; EVALUATION &amp; CHECKPOINTING
&#35; ====================
save_strategy: steps
save_steps: 20
eval_steps: 35
save_total_limit: 18  &#35; Keep best + last few checkpoints
load_best_model_at_end: true
metric_for_best_model: eval_loss
greater_is_better: false
<br>
&#35; ====================
&#35; LOGGING &amp; OUTPUT
&#35; ====================
output_dir: ./GLM-AIR-SFT_v2-5
logging_steps: 1
save_safetensors: true
<br>
&#35; ====================
&#35; WANDB TRACKING
&#35; ====================
wandb_project: GLM-AIR-SFT
&#35; wandb_entity: your_entity
wandb_name: GLM-AIR-SFT_v2-5</code></pre>
          </div>
        </details>
      </div>
    </div>
  </div>
</div>
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</html>