osk-arr00/LFM2.5-8B-A1B-ThinkingCap-GGUF overview

---

language:

• en
• es

license: other

license_name: lfm1.0

license_link: https://huggingface.co/LiquidAI/LFM2.5-8B-A1B/blob/main/LICENSE

pipeline_tag: text-generation

tags:

• llama.cpp
• gguf
• rocm
• rocmfpx
• apex
• strix-halo
• gfx1151
• thinkingcap
• reasoning
• tool-calling
• dspark

base_model: osk-arr00/LFM2.5-8B-A1B-ThinkingCap

---

<div align="center">

<h1>🧠 LFM 2.5 8B ThinkingCap — GGUF & ROCmFPX Suite</h1>

<p><strong>Abliterated • SFT + DPO ThinkingCap • APEX Tensor-Mapped • Native ROCmFP4 for AMD Strix Halo (gfx1151)</strong></p>

</div>

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📌 Model Overview

LFM 2.5 8B ThinkingCap is a distilled, reasoning-calibrated, and abliterated hybrid architecture combining 36 layers of 1D short-convolutions with a 32-expert MoE backbone (~1.2B active parameters per token out of 8.47B total).

Trained using full Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO), this model enforces the ThinkingCap contract:

• Focused internal reasoning enclosed strictly within [Start thinking] ... [End thinking].
• Clean, structured, and deterministic answers in the deliverable block.
• High fidelity and fluency in English and Spanish.

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⚡ Quantization & APEX Matrix

This repository provides standard GGUF quantizations alongside APEX mixed-precision and native ROCmFP4 quants specifically tailored for the AMD Strix Halo (RDNA 3.5 / gfx1151 / Radeon 8050S / Radeon 8060S) platform with unified LPDDR5X memory.

Available GGUF Files

| File Name | Size | Type / Description | Target Architecture |

| :--- | :---: | :--- | :--- |

| LFM2.5-8B-A1B-ThinkingCap-ROCMFPX-APEX.gguf | 4.78 GB | Definitive ROCmFPX APEX: Routed experts in Q4_0_ROCMFP4, attention in Q6_K, shared experts/conv in Q8_0 | AMD Strix Halo (gfx1151) ROCmFPX |

| LFM2.5-8B-A1B-ThinkingCap-APEX.gguf | 4.63 GB | Universal APEX: Routed experts in Q4_K_M + apex.txt + iMatrix, attention in Q6_K, conv in Q8_0 | llama.cpp / Vulkan / CPU / CUDA |

| LFM2.5-8B-A1B-ThinkingCap-APEX-Q6.gguf | 6.60 GB | APEX Q6: Maximum reasoning retention with moderate compression | High-precision inference |

| LFM2.5-8B-A1B-ThinkingCap-Q8_0.gguf | 8.40 GB | Full 8-bit reference | Reference evaluation |

| LFM2.5-8B-A1B-ThinkingCap-Q6_K.gguf | 6.50 GB | Standard Q6_K | General purpose |

| LFM2.5-8B-A1B-ThinkingCap-BF16.gguf | 16.0 GB | Unquantized BF16 Base | Research / Conversion base |

| LFM2.5-8B-A1B-DSpark-Q4_K_M.gguf | 191 MB | DSpark Speculative Drafter (Q4_K_M) | Speculative decoding drafter |

| LFM2.5-8B-A1B-DSpark-BF16.gguf | 633 MB | DSpark Drafter (BF16) | Speculative decoding base |

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📊 Measured Benchmarks (AMD Radeon 8050S / Strix Halo gfx1151)

All benchmarks measured locally on AMD Ryzen AI Max 385 APU with 64 GB LPDDR5X Unified RAM.

1. Prefill & Generation Context Ladder (llama-bench)

| Metric | Context Size | GPU Performance |

| :--- | :---: | :---: |

| Short Prefill (pp512) | 512 tokens | 3,061.1 tokens/sec |

| Medium Prefill (pp1024) | 1,024 tokens | 3,165.7 tokens/sec |

| Long Prefill (pp4096) | 4,096 tokens | 3,564.4 tokens/sec |

| Deep Prefill (pp16384) | 16,384 tokens | 2,233.8 tokens/sec |

| Decode / Generation (Single-Stream) | 128 – 4,096 tokens | 115.8 – 148.3 tokens/sec |

2. Multi-Slot Continuous Batching Scaling (256K Global Context)

| Concurrency | Per-User Decode Speed | Aggregate System Throughput | Scaling Factor |

| :---: | :---: | :---: | :---: |

| 1 Slot | 148.3 tokens/sec | 121.5 tokens/sec | 1.00× (Baseline) |

| 2 Slots | 75.2 tokens/sec | 136.4 tokens/sec | 1.12× |

| 4 Slots | 64.8 tokens/sec | 211.8 tokens/sec | 1.74× |

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🎯 Recommended Sampling Parameters (DRY Sampler Sweetspot)

For optimal reasoning output and zero repetitive loops, use the DRY (Don't Repeat Yourself) sampler configuration instead of static repetition penalty:

{
  "temperature": 0.2,
  "top_p": 0.95,
  "dry_multiplier": 0.8,
  "dry_base": 1.75,
  "dry_allowed_length": 2,
  "max_tokens": -1
}

---

🚀 Quickstart & Deployment

Run with llama-cli (ROCm / HIP for gfx1151)

export HSA_OVERRIDE_GFX_VERSION=11.5.1
export GGML_HIP_ENABLE_UNIFIED_MEMORY=1

llama-cli \
  -m LFM2.5-8B-A1B-ThinkingCap-ROCMFPX-APEX.gguf \
  -dev ROCm0 \
  -ngl 999 \
  -fa on \
  -ctk q8_0 -ctv q8_0 \
  -b 8192 -ub 2048 \
  -c 32768 \
  --dry-multiplier 0.8 --dry-base 1.75 --dry-allowed-length 2 \
  --temp 0.2 \
  --jinja \
  -p "<|im_start|>system\nEres un asistente experto.\n<|im_end|>\n<|im_start|>user\nHola, ¿cuál es tu arquitectura?\n<|im_end|>\n<|im_start|>assistant\n"

Production Server (Docker)

docker run -d \
  --name strix-halo-server-fast \
  --restart unless-stopped \
  --device=/dev/dri \
  --group-add 983 --group-add 987 \
  -p 8080:8080 \
  -v /path/to/models:/models:ro \
  strix-halo-llamacpp:ultimate-vulkan \
  llama-server \
    -m /models/LFM2.5-8B-A1B-ThinkingCap-ROCMFPX-APEX.gguf \
    -ngl 999 \
    --host 0.0.0.0 \
    --port 8080 \
    -fa 1 \
    -ctk q8_0 -ctv q8_0 \
    -b 16384 -ub 2048 \
    -np 4 -c 262144 \
    --jinja \
    --metrics

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📄 License & Credits

• Base Weights: Based on Liquid AI LFM 2.5 8B A1B and huihui-ai abliterated.
• Fine-Tuning: ThinkingCap SFT & DPO training by osk-arr00.
• ROCmFPX Kernels: Built with ROCmFP4 custom Wave32 ISA kernels for AMD RDNA 3.5 (gfx1151).