Instructions to use YourHighnessLA/Tess-4-27B-EAGLE3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YourHighnessLA/Tess-4-27B-EAGLE3-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="YourHighnessLA/Tess-4-27B-EAGLE3-GGUF",
	filename="Tess-4-27B-EAGLE3-BF16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use YourHighnessLA/Tess-4-27B-EAGLE3-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 YourHighnessLA/Tess-4-27B-EAGLE3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf YourHighnessLA/Tess-4-27B-EAGLE3-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf YourHighnessLA/Tess-4-27B-EAGLE3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf YourHighnessLA/Tess-4-27B-EAGLE3-GGUF:Q4_K_M

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 YourHighnessLA/Tess-4-27B-EAGLE3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf YourHighnessLA/Tess-4-27B-EAGLE3-GGUF:Q4_K_M

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 YourHighnessLA/Tess-4-27B-EAGLE3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf YourHighnessLA/Tess-4-27B-EAGLE3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/YourHighnessLA/Tess-4-27B-EAGLE3-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use YourHighnessLA/Tess-4-27B-EAGLE3-GGUF with Ollama:
```
ollama run hf.co/YourHighnessLA/Tess-4-27B-EAGLE3-GGUF:Q4_K_M
```

Unsloth Studio

How to use YourHighnessLA/Tess-4-27B-EAGLE3-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 YourHighnessLA/Tess-4-27B-EAGLE3-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 YourHighnessLA/Tess-4-27B-EAGLE3-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for YourHighnessLA/Tess-4-27B-EAGLE3-GGUF to start chatting

How to use YourHighnessLA/Tess-4-27B-EAGLE3-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YourHighnessLA/Tess-4-27B-EAGLE3-GGUF:Q4_K_M

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": "YourHighnessLA/Tess-4-27B-EAGLE3-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use YourHighnessLA/Tess-4-27B-EAGLE3-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 YourHighnessLA/Tess-4-27B-EAGLE3-GGUF:Q4_K_M

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 YourHighnessLA/Tess-4-27B-EAGLE3-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use YourHighnessLA/Tess-4-27B-EAGLE3-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YourHighnessLA/Tess-4-27B-EAGLE3-GGUF:Q4_K_M

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 "YourHighnessLA/Tess-4-27B-EAGLE3-GGUF:Q4_K_M" \
  --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"

Docker Model Runner
How to use YourHighnessLA/Tess-4-27B-EAGLE3-GGUF with Docker Model Runner:
```
docker model run hf.co/YourHighnessLA/Tess-4-27B-EAGLE3-GGUF:Q4_K_M
```

Lemonade

How to use YourHighnessLA/Tess-4-27B-EAGLE3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull YourHighnessLA/Tess-4-27B-EAGLE3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Tess-4-27B-EAGLE3-GGUF-Q4_K_M

List all available models

lemonade list

Tess-4-27B EAGLE-3 Draft — GGUF

GGUF conversions of migtissera/Tess-4-27B-EAGLE3, the EAGLE-3 speculative-decoding draft head for migtissera/Tess-4-27B, for use with mainline llama.cpp's external EAGLE-3 path (--spec-type draft-eagle3).

File	Size	Notes
`Tess-4-27B-EAGLE3-Q4_K_M.gguf`	403 MB	Recommended — acceptance is flat across draft quants, so the smallest draft wins on latency
`Tess-4-27B-EAGLE3-Q8_0.gguf`	629 MB
`Tess-4-27B-EAGLE3-BF16.gguf`	1.22 GB	Direct converter output

Usage

llama-server \
  -m Tess-4-27B-Q4_K_M.gguf \
  --spec-type draft-eagle3 \
  --spec-draft-model Tess-4-27B-EAGLE3-Q4_K_M.gguf \
  --spec-draft-n-max 2

--spec-draft-n-max 2 is the measured sweet spot on this head — deeper drafting is strictly worse (per-position acceptance decays; rejected drafts waste target verification). Speculative decoding is output-lossless: the target verifies every token, confirmed at benchmark granularity (150-scenario quality suite, within run-to-run noise of no-spec and of a DFlash draft).

Measured performance

Acceptance on this head is modest — it accelerates Tess-4, but a stronger draft beats it where one exists:

Rig	Config	Acceptance	Throughput
1× RTX 4090 (Q4_K_M target, ctx 98K)	n_max=2	49.1%	~76–78 code tok/s (vs 50.4 no-spec — 1.55×)
1× RTX 4090	n_max=4	27.4–28.0% (flat across BF16/Q8_0/Q4_K_M draft)	62–71 tok/s
2× RTX 3090 (club-3090, llama.cpp b9967)	n=2	40.8%	47.5 narr / 61.3 code tok/s

For context on the same rigs, ~0.70-acceptance heads (DFlash, external MTP) reach 1.9–2×. See noonghunna/club-3090#662 for forensics on this head's acceptance ceiling and #665 / PR #674 for the quality benchmarks behind these numbers. Notably, this head's acceptance is feed-robust: it scores in the same band fed hidden states (vLLM/SGLang, in-graph) or fed tokens (llama.cpp external path).

Conversion recipe

Converted at llama.cpp b9932 (a646006f0); independently reproduced at b9967 — the recipe is version-robust across that span.

# The target dir needs ONLY the base model's config.json + tokenizer files
# (~12 MB download) — no 27B weights required.
python convert_hf_to_gguf.py <Tess-4-27B-EAGLE3-dir> \
  --target-model-dir <Tess-4-27B-metadata-dir>          # → BF16 GGUF
llama-quantize Tess-4-27B-EAGLE3-BF16.gguf Tess-4-27B-EAGLE3-Q4_K_M.gguf Q4_K_M
llama-quantize Tess-4-27B-EAGLE3-BF16.gguf Tess-4-27B-EAGLE3-Q8_0.gguf Q8_0

The converter reads the target's hidden size (5120) from its config, selects extraction layers [2, 32, 61], and writes the 32K→248K d2t vocab map into the GGUF (eagle3.target_layers / eagle3.target_hidden_size metadata, inspectable with llama-gguf).

License

Apache 2.0, matching the source head and Tess-4-27B. Draft head by Migel Tissera; GGUF conversion and llama.cpp benchmarks as credited above.

Downloads last month: 690

GGUF

Model size

0.6B params

Architecture

eagle3

Hardware compatibility

4-bit

8-bit

16-bit

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