Instructions to use ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF",
	filename="Laguna-XS-2.1-DFlash-ik_llama-Q8_0.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 ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-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 ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF:Q8_0

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 ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF:Q8_0

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 ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF:Q8_0

Use Docker

docker model run hf.co/ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF with Ollama:
```
ollama run hf.co/ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF:Q8_0
```

Unsloth Studio

How to use ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-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 ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-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 ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF to start chatting

How to use ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF:Q8_0

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": "ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-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 ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF:Q8_0

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 ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF:Q8_0

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 "ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF:Q8_0" \
  --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 ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF with Docker Model Runner:
```
docker model run hf.co/ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF:Q8_0
```

Lemonade

How to use ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ji-farthing/Laguna-XS-2.1-DFlash-ik-llama-GGUF:Q8_0

Run and chat with the model

lemonade run user.Laguna-XS-2.1-DFlash-ik-llama-GGUF-Q8_0

List all available models

lemonade list

Laguna-XS-2.1-DFlash-ik-llama-GGUF / README.md

ji-farthing

Upload README.md with huggingface_hub

9fd4740 verified 9 days ago

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metadata

license: openmdw-1.1
base_model:
  - poolside/Laguna-XS-2.1-DFlash
  - poolside/Laguna-XS-2.1
tags:
  - gguf
  - ik_llama
  - dflash
  - speculative-decoding
  - laguna
library_name: gguf
inference: false

Laguna-XS-2.1-DFlash (ik_llama.cpp GGUF)

A GGUF conversion of poolside/Laguna-XS-2.1-DFlash, the DFlash speculative drafter for poolside/Laguna-XS-2.1. It is published so the Laguna DFlash support proposed for ik_llama.cpp can be tested without re-running the conversion.

This is not a standalone model. It only works as a DFlash draft passed with --model-draft alongside the Laguna XS 2.1 target. Loading it on its own produces nothing useful.

It requires the Laguna DFlash branch of ik_llama.cpp (the arch is not in mainline yet). The pull request adds the converter, loader, and graph support for DFlashLagunaForCausalLM; a stock build will not load this file.

Files

File	Quant	Size	sha256
`Laguna-XS-2.1-DFlash-ik_llama-Q8_0.gguf`	Q8_0	472 MiB	`a23611d163f2f3709ee04aba…`

Matching target

Pair it with a GGUF of the target model, poolside/Laguna-XS-2.1, converted with the same branch. The runtime numbers below used a Q4_K_M target.

Usage

llama-cli -m Laguna-XS-2.1-Q4_K_M.gguf \
  -md Laguna-XS-2.1-DFlash-ik_llama-Q8_0.gguf \
  --spec-type dflash:n_max=2,cross_ctx=512 \
  -ngl 999 -ot exps=CPU -ngld 999 --jinja \
  -p "Write a quick sort implementation in python."

The proposal depth that pays off depends on where the target runs. n_max=2 is a reasonable default across setups. With the target fully on CPU the optimum is higher (n_max=4); with the target offloaded to CUDA it is lower (n_max=1-2), and the draft should also sit on GPU (-ngld 999), because a CPU-side draft stalls the fast target and loses at every depth.

The speedup is largest on structured output (code, tables) where the drafter predicts well, and can turn into a slowdown on free prose where it does not. Measure on your own workload before committing to a setting.

Validation

Conversion (deterministic):

Check	Result
Tensors emitted	68
Target/draft tokenizer token + merge SHA	match
`gguf-py` tests	5 passed

Runtime smoke, Q8_0 draft with a Q4_K_M target, greedy, RTX 4070 + Core i7-11700K. Token generation, speculative vs non-speculative on the same prompt:

Target placement	Setting	no-spec t/s	DFlash t/s	ratio
CPU	code, `n_max=4`	19.7	35.9	1.82x
CPU	code, `n_max=4` (quicksort)	20.0	29.9	1.50x
CPU	prose, `n_max=4`	20.8	16.6	0.80x
CUDA (experts on CPU)	code, `n_max=2`	46.7	51.3	1.10x

Output is coherent and correct: generated code compiles and prose stays accurate. It is not guaranteed bit-identical to non-speculative decoding on longer greedy runs, because batched block verification occasionally flips a near-tie argmax in the target. This is a property of the target model under batched evaluation, not of the draft; generic (non-Laguna) DFlash drafts are unaffected.

This is a functional evaluation with a quantized pair, not a substitute for poolside's own BF16 chat-template evaluation.

Conversion notes

Draft source: poolside/Laguna-XS-2.1-DFlash
Target source: poolside/Laguna-XS-2.1
Converted with the DFlashLagunaForCausalLM path added in the ik_llama.cpp Laguna DFlash pull request. The converter splits the packed QKV, writes the per-slice auxiliary RMS norms and per-layer attention gates, and records the causal all-SWA contract that the loader and graph read back.

License

The source model is released under the OpenMDW-1.1 license, included here as LICENSE.md. That license is retained with this distribution as it requires.