Instructions to use ji-farthing/Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ji-farthing/Qwen3.5-35B-A3B-DFlash-SWA-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/Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF",
	filename="Qwen3.5-35B-A3B-DFlash-SWA-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/Qwen3.5-35B-A3B-DFlash-SWA-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/Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf ji-farthing/Qwen3.5-35B-A3B-DFlash-SWA-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/Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf ji-farthing/Qwen3.5-35B-A3B-DFlash-SWA-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/Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf ji-farthing/Qwen3.5-35B-A3B-DFlash-SWA-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/Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ji-farthing/Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF:Q8_0

Use Docker

docker model run hf.co/ji-farthing/Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use ji-farthing/Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF with Ollama:
```
ollama run hf.co/ji-farthing/Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF:Q8_0
```

Unsloth Studio

How to use ji-farthing/Qwen3.5-35B-A3B-DFlash-SWA-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/Qwen3.5-35B-A3B-DFlash-SWA-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/Qwen3.5-35B-A3B-DFlash-SWA-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/Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF to start chatting

How to use ji-farthing/Qwen3.5-35B-A3B-DFlash-SWA-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/Qwen3.5-35B-A3B-DFlash-SWA-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/Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ji-farthing/Qwen3.5-35B-A3B-DFlash-SWA-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/Qwen3.5-35B-A3B-DFlash-SWA-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/Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ji-farthing/Qwen3.5-35B-A3B-DFlash-SWA-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/Qwen3.5-35B-A3B-DFlash-SWA-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/Qwen3.5-35B-A3B-DFlash-SWA-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/Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF with Docker Model Runner:
```
docker model run hf.co/ji-farthing/Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF:Q8_0
```

Lemonade

How to use ji-farthing/Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ji-farthing/Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF:Q8_0

Run and chat with the model

lemonade run user.Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF-Q8_0

List all available models

lemonade list

Qwen3.5-35B-A3B DFlash SWA draft for ik_llama

This repo contains an ik_llama-compatible DFlash draft GGUF converted from z-lab/Qwen3.5-35B-A3B-DFlash, carrying the per-layer sliding-window attention (SWA) pattern.

This is not a standalone chat model. Use it as a --model-draft file next to a matching Qwen3.5-35B-A3B target GGUF, with DFlash speculative decoding.

Sliding-window attention

The draft is sliding-window on every layer except a final full-attention (global) layer: sliding_window_pattern = [true, true, true, true, true, false], sliding_window = 4096.

Files

File	Quant	Draft window
`Qwen3.5-35B-A3B-DFlash-SWA-ik_llama-Q8_0.gguf`	Q8_0	4096 (5 sliding + 1 global)

Use

llama-server \
  -m /path/to/Qwen3.5-35B-A3B-<quant>.gguf \
  --model-draft /path/to/Qwen3.5-35B-A3B-DFlash-SWA-ik_llama-Q8_0.gguf \
  --spec-type dflash:n_max=4,cross_ctx=8192 \
  -c 8192

SWA only engages once the DFlash cross-context exceeds the 4096 window, so set cross_ctx above the window for long-context prompts (the default 512 does not grow with -c).

Validation (RTX 4070, ik_llama DFlash SWA branch)

Draft acceptance and throughput versus the same draft run with full attention, as the prompt overflows the 4096 window, where clip = (prompt - 4096) / prompt:

prompt tok	clip	accept, full-attn	accept, SWA	acceptance gain	tok/s change
37	0%	36.1%	39.2%	+3.0 pp	+6.8%
5613	27%	28.4%	28.6%	+0.2 pp	-1.1%
8044	49%	22.3%	27.7%	+5.4 pp	+9.9%
11005	63%	15.9%	26.4%	+10.5 pp	+23%
19946	80%	5.5%	23.0%	+17.4 pp	+44%

The benefit grows with how far the prompt overflows the window and does not saturate.

Conversion

Converted from z-lab/Qwen3.5-35B-A3B-DFlash with ik_llama's convert_hf_to_gguf.py DFlash draft converter (sliding-window support branch), then quantized to Q8_0. The per-layer SWA pattern is taken from the source layer_types. Conversion requires a --target-model-dir containing the target tokenizer merges.

Downloads last month: 116

GGUF

Model size

0.4B params

Architecture

dflash-draft

Hardware compatibility

8-bit

Inference Providers NEW

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Model tree for ji-farthing/Qwen3.5-35B-A3B-DFlash-SWA-ik-llama-GGUF

Base model

Qwen/Qwen3.5-35B-A3B-Base

Finetuned

Qwen/Qwen3.5-35B-A3B

Quantized

(277)

this model