Instructions to use lmcoleman/Tess-4-27B-ROCmFPX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use lmcoleman/Tess-4-27B-ROCmFPX-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./llama-cli -hf lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP

Use Docker

docker model run hf.co/lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP

LM Studio
Jan

vLLM

How to use lmcoleman/Tess-4-27B-ROCmFPX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lmcoleman/Tess-4-27B-ROCmFPX-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lmcoleman/Tess-4-27B-ROCmFPX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP

Ollama
How to use lmcoleman/Tess-4-27B-ROCmFPX-GGUF with Ollama:
```
ollama run hf.co/lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP
```

Unsloth Studio

How to use lmcoleman/Tess-4-27B-ROCmFPX-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for lmcoleman/Tess-4-27B-ROCmFPX-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for lmcoleman/Tess-4-27B-ROCmFPX-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lmcoleman/Tess-4-27B-ROCmFPX-GGUF to start chatting

How to use lmcoleman/Tess-4-27B-ROCmFPX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use lmcoleman/Tess-4-27B-ROCmFPX-GGUF with Docker Model Runner:
```
docker model run hf.co/lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP
```

Lemonade

How to use lmcoleman/Tess-4-27B-ROCmFPX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.Tess-4-27B-ROCmFPX-GGUF-Q4_0_ROCMFP

List all available models

lemonade list

Hermes Agent

How to use lmcoleman/Tess-4-27B-ROCmFPX-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use lmcoleman/Tess-4-27B-ROCmFPX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "lmcoleman/Tess-4-27B-ROCmFPX-GGUF:Q4_0_ROCMFP" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Tess-4-27B-ROCmFPX-GGUF / README.md

lmcoleman

Name the known-good ROCmFPX fork commit these files were built with

0944ddd verified 20 days ago

preview code

Raw

History Blame Contribute Delete

4.09 kB

metadata

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

Generated with Foundry