---
library_name: transformers
license: apache-2.0
base_model: LiquidAI/LFM2.5-8B-A1B
base_model_relation: finetune
tags:
  - lfm
  - liquid-ai
  - moe
  - agentic
  - terminal
  - fable-5
  - distillation
  - sft
  - ablation
language:
  - en
pipeline_tag: text-generation
---

# Fabliq-8B-Agent-FromBase 🌊🔬

> **Ablation variant** of [Fabliq-8B-Agent](https://huggingface.co/LLM-OS-Models/Fabliq-8B-Agent) — fine-tuned directly from raw [`LiquidAI/LFM2.5-8B-A1B`](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B), skipping the ToolBench foundation. Used to isolate the effect of the ToolBench intermediate stage.

## 🔬 Why this variant?

[Fabliq-8B-Agent](https://huggingface.co/LLM-OS-Models/Fabliq-8B-Agent) is trained as: `LiquidAI/LFM2.5-8B-A1B` → `ToolBench-Full-SFT-1Epoch` → `Fable-5`.

**This model** skips the middle step: `LiquidAI/LFM2.5-8B-A1B` → `Fable-5` (direct).

Comparing the two answers the question: **does ToolBench foundation actually help, or does Fable-5 alone give you the same agent?**

## 🧪 Model details

| | |
| --- | --- |
| **Architecture** | Lfm2MoeForCausalLM (24 layers, 32 experts, 4 experts/token) |
| **Parameters** | ~8B total / ~1B active (MoE) |
| **Context** | 8,192 trained · 128K native |
| **Precision** | bfloat16 |
| **Fine-tune type** | Full-parameter SFT (direct from base, no ToolBench) |
| **License** | Apache 2.0 |

## 📚 Training data

Same as Phase-1: [Fable-5-traces](https://huggingface.co/datasets/Glint-Research/Fable-5-traces), 4,047 rows × 3 epoch.

## 🔧 Training procedure

| Hyperparameter | Value |
| --- | --- |
| Schedule | 3 epochs, constant LR |
| Max sequence length | 8,192 |
| Per-device batch size | 2 |
| Gradient accumulation | 4 |
| GPUs | 8× H200 (effective batch 64) |
| Learning rate | **1e-6** (higher than Phase-1's 5e-7, since starting from base) |
| Precision | bf16 |
| Final train_loss | logged in run_config.json |
| Train runtime | ~14 min |

## 🌳 Model tree

```
LiquidAI/LFM2.5-8B-A1B
  ├─ LLM-OS-Models/LFM2.5-8B-A1B-Terminal-ToolBench-Full-SFT-1Epoch
  │   └─ LLM-OS-Models/Fabliq-8B-Agent          ← Phase-1 (ToolBench → Fable-5)
  └─ LLM-OS-Models/Fabliq-8B-Agent-FromBase     ← this model (base → Fable-5, direct)
      └─ LLM-OS-Models/Fabliq-8B-Agent-FromBase-Reasoning  ← + reasoning expansion
```

## 🚀 Usage

See [Fabliq-8B-Agent](https://huggingface.co/LLM-OS-Models/Fabliq-8B-Agent) — same system prompt, same tool-call format, same inference code.

## 📜 License

Apache 2.0.