---

license: apache-2.0

library_name: llama.cpp

base_model:

• migtissera/Tess-4-27B

base_model_relation: quantized

pipeline_tag: text-generation

quantized_by: ROCmFPX

language:

• en

tags:

• gguf
• rocm
• amd
• strix-halo
• gfx1151
• rocmfpx

---

Tess-4-27B-ROCmFPX-GGUF

> ## ⚠️ These files do NOT load on standard llama.cpp

> They use AMD-native *_ROCMFPX tensor types from the experimental

> ciru-ai/ROCmFPX llama.cpp fork (build from source).

Derivative of Tess-4-27B, quantized to AMD-native ROCmFPX formats (fork-only) tuned for Strix Halo (gfx1151).

Base Model

This is a derivative of Tess-4-27B.

All credit for the base model architecture and weights goes to the original authors.

The base model's license applies to this derivative.

ROCmFPX (AMD-native, fork-only)

These GGUFs use AMD-native quantization schemes from the experimental

ciru-ai/ROCmFPX llama.cpp fork,

tuned for and benchmarked on AMD Strix Halo (Radeon 8060S iGPU, gfx1151, unified memory):

• ROCmFP3/4/6/8 tensor types with straight and "agent" presets (agent presets keep

tool-calling / JSON-structured output reliable at low bit-widths)

• Files load only on the fork -- build it from source. Known-good commit these files were built and validated with:

git clone https://github.com/ciru-ai/ROCmFPX && cd ROCmFPX
git checkout 221402af8574faf652b101b6afe225a3f329561f

GGUF Files

| File | Size | Quant |

|------|------|-------|

| Tess-4-27B-Q3_0_ROCMFPX.gguf | 15.1 GB | ROCmFP3 (fork-only) |

| Tess-4-27B-Q3_0_ROCMFPX_AGENT.gguf | 19.5 GB | ROCmFP3 (fork-only), agent preset |

| Tess-4-27B-Q4_0_ROCMFP4.gguf | 17.7 GB | ROCmFP4 (fork-only) |

| Tess-4-27B-Q4_0_ROCMFP4_COHERENT.gguf | 15.7 GB | ROCmFP4 (fork-only), coherent preset |

| Tess-4-27B-Q6_0_ROCMFPX.gguf | 22.5 GB | ROCmFP6 (fork-only) |

| Tess-4-27B-Q6_0_ROCMFPX_AGENT.gguf | 25.3 GB | ROCmFP6 (fork-only), agent preset |

| Tess-4-27B-Q8_0_ROCMFPX.gguf | 28.2 GB | ROCmFP8 (fork-only) |

| Tess-4-27B-Q8_0_ROCMFPX_AGENT.gguf | 28.7 GB | ROCmFP8 (fork-only), agent preset |

Usage

Requires a from-source build of the ROCmFPX fork

(stock llama.cpp, LM Studio, and Ollama cannot load these files):

# Interactive chat
llama-cli -m Tess-4-27B-Q3_0_ROCMFPX.gguf -c 8192 -cnv

# Server mode
llama-server -m Tess-4-27B-Q3_0_ROCMFPX.gguf -c 8192 --port 8080 -ngl 99 -fa on

Serving: MTP Speculative Decoding

This model includes MTP ("nextn") draft tensors, enabling self-speculative

decoding -- measured ~1.6-1.9x faster generation with a ~95% first-token

accept rate (no separate draft model needed; it drafts from itself):

llama-server -m Tess-4-27B-Q3_0_ROCMFPX.gguf -c 8192 --port 8080 --host 127.0.0.1 -ngl 99 -md Tess-4-27B-Q3_0_ROCMFPX.gguf --spec-type draft-mtp -ctk q8_0 -ctv q8_0 -fa on

Memory cost: MTP needs its own draft context alongside the main context,

so serving with it uses roughly 2x the model's memory compared to serving

without `-md/--spec-type draft-mtp`.

Caveats

• The base model's license (apache-2.0) applies to all derivative files
• Fork-only files: stock llama.cpp, LM Studio, and Ollama cannot load these -- build ciru-ai/ROCmFPX from source
• Quantization reduces precision -- verify outputs for your specific use case

Limitations

• Quantized models may exhibit subtle differences from the full-precision fine-tune
• This model inherits any limitations and biases present in the base model

---

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