Fabliq-8B-Agent-Reasoning

Fabliq-8B-Agent-Reasoning 🌊🧠

The reasoning-expanded sibling of Fabliq-8B-Agent. Adds general + deep reasoning on top of the agentic foundation — broadens the model beyond pure terminal tool-use into multi-domain expert Q&A, mathematical reasoning, scientific analysis, and cybersecurity. Two-phase curriculum inspired by Qwythos-9B.

✨ Why Fabliq-Reasoning?

• 🐠 Same tiny footprint, broader reach. Inherits LFM2.5-8B-A1B's MoE efficiency (~1B active params). Now also handles expert Q&A, math, science — not just terminal work.
• 🛠 Still agentic. Phase-1 tool-use capability is preserved — the model still reasons in <think> and emits native LFM tool calls when needed.
• 🧠 Multi-domain reasoning. Trained on WithinUs (6 categories: advanced coding, agentic planning, general QA, math reasoning, scientific analysis, cybersecurity) + Helio (Opus 4.8 deep-reasoning distillation).
• 🎯 2-phase curriculum. Phase-1 broad agentic distillation → Phase-2 focused reasoning expansion (Qwythos pattern).

🧪 Model details

📚 Training data (Phase-2 only)

Preprocessing:

• WithinUs: Category-balanced (max 350/cat), SHA-256 dedup, "Drawing from the autonomous..." template first-sentence removal → build_withinus_lfm_sft.py
• Helio: Cyrillic ratio filter (<30%), <think> wrapping for reasoning, line 192 corruption skip → build_helio_lfm_sft.py
• Combined: build_phase2_reasoning (concat)

🔧 Training procedure (Phase-2)

💬 System prompts (per data source)

WithinUs (broad reasoning):

You are a knowledgeable assistant. Provide rigorous, well-structured answers
across coding, cybersecurity, mathematics, scientific analysis, agentic planning,
and general expert topics. Be precise and thorough.

Helio (deep reasoning):

You are a deep-reasoning assistant. Think step by step inside <think>...</think>,
then provide a clear, structured answer.

🚀 How to use

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "LLM-OS-Models/Fabliq-8B-Agent-Reasoning"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, dtype=torch.bfloat16, device_map="auto"
)

SYSTEM = (
    "You are a deep-reasoning assistant. Think step by step inside <think>...</think>, "
    "then provide a clear, structured answer."
)

messages = [
    {"role": "system", "content": SYSTEM},
    {"role": "user", "content": "Derive the time complexity of merge sort and explain when it beats quicksort."},
]
text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(text, return_tensors="pt").to(model.device)

out = model.generate(
    **inputs,
    max_new_tokens=2048,
    do_sample=False,
    repetition_penalty=1.05,
)
print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False))

🎯 When to use which Fabliq?

⚠️ Limitations

• Phase-2 dataset is small (281 rows). Reasoning expansion is real but bounded — this is a delta on top of Phase-1, not a from-scratch reasoning model.
• WithinUs dedup surprise. Source dataset claims 25k rows but after SHA-256 dedup of templated prompts, only 135 unique rows remain. Template overfitting in the source data was severe.
• Helio Russian filter. Original 462 rows filtered to 146 rows after removing Cyrillic-dominant (Russian) traces. Non-English coverage is limited.
• No safety alignment. Trained on raw reasoning traces; add your own guardrails for production.
• Max seq 8,192 at training. Behavior beyond 8K context is unverified.
• English-centric.

📜 License

Apache 2.0, inherited from the LiquidAI LFM2.5-8B-A1B base.

🌳 Model tree

This is a fine-tune (continuation SFT). Direct parent: LLM-OS-Models/Fabliq-8B-Agent.

LiquidAI/LFM2.5-8B-A1B                                          (LiquidAI base)
  └─ LLM-OS-Models/LFM2.5-8B-A1B-Terminal-ToolBench-Full-SFT-1Epoch  (ToolBench foundation)
      └─ LLM-OS-Models/Fabliq-8B-Agent                           (Phase-1: Fable-5 agentic SFT)
          └─ LLM-OS-Models/Fabliq-8B-Agent-Reasoning             ← this model (Phase-2: + WithinUs + Helio)

🙏 Acknowledgements

• Base: LiquidAI/LFM2.5-8B-A1B
• Phase-1 parent: Fabliq-8B-Agent
• Phase-2 data: WithinUs (claude_mythos_distilled_25k), Helio (Fable-5-Distill-Reasoning-462x)
• Reference: empero-ai/Qwythos-9B-Claude-Mythos-5-1M — 2-phase curriculum pattern