Instructions to use empero-ai/Qwen3.8-35B-A3B-Distill-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 empero-ai/Qwen3.8-35B-A3B-Distill-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 empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf empero-ai/Qwen3.8-35B-A3B-Distill-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 empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf empero-ai/Qwen3.8-35B-A3B-Distill-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 empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf empero-ai/Qwen3.8-35B-A3B-Distill-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 empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

Use Docker

docker model run hf.co/empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use empero-ai/Qwen3.8-35B-A3B-Distill-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "empero-ai/Qwen3.8-35B-A3B-Distill-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": "empero-ai/Qwen3.8-35B-A3B-Distill-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

Ollama
How to use empero-ai/Qwen3.8-35B-A3B-Distill-GGUF with Ollama:
```
ollama run hf.co/empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M
```
Unsloth Desktop

How to use empero-ai/Qwen3.8-35B-A3B-Distill-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use empero-ai/Qwen3.8-35B-A3B-Distill-GGUF with Docker Model Runner:
```
docker model run hf.co/empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M
```

Lemonade

How to use empero-ai/Qwen3.8-35B-A3B-Distill-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-35B-A3B-Distill-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use empero-ai/Qwen3.8-35B-A3B-Distill-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 empero-ai/Qwen3.8-35B-A3B-Distill-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 empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use empero-ai/Qwen3.8-35B-A3B-Distill-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf empero-ai/Qwen3.8-35B-A3B-Distill-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 "empero-ai/Qwen3.8-35B-A3B-Distill-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"

empero-ai commited on 16 days ago

Commit

b1f9d1d

verified ·

1 Parent(s): 51bdd06

Add IQ2_M, Q2_K, IQ3_M, Q3_K_M, IQ4_XS (imatrix-calibrated); update file table and checksums

Browse files

Files changed (2) hide show

README.md +10 -1
SHA256SUMS +6 -1

README.md CHANGED Viewed

@@ -36,6 +36,11 @@ This card is about choosing a file and running it. The capability writeup, bench
 | File | Quant | Size | Notes |
 |---|---|---:|---|
 | `Qwen3.8-35B-A3B-Q4_K_M.gguf` | Q4_K_M | 21.713 GB | **Recommended.** Best quality/size balance for most users. |
 | `Qwen3.8-35B-A3B-Q5_K_M.gguf` | Q5_K_M | 25.348 GB | Higher quality, modest size increase. |
 | `Qwen3.8-35B-A3B-Q6_K.gguf` | Q6_K | 29.209 GB | Near-lossless. |
@@ -43,6 +48,8 @@ This card is about choosing a file and running it. The capability writeup, bench
 | `Qwen3.8-35B-A3B-BF16.gguf` | BF16 | 71.067 GB | Full precision reference. |
 | `mmproj-Qwen3.8-35B-A3B-F16.gguf` | F16 | 0.899 GB | Vision projector. Pair with any text quant above for image input. |
 Sizes are exact decimal GB from the uploaded files (1 GB = 1,000,000,000 bytes).
 ### What fits?
@@ -51,7 +58,9 @@ Weight-size guidance at modest context. The KV cache is the dominant cost at lon
 | Quant | Guidance |
 |---|---|
-| Q4_K_M | 24 GB VRAM for a full GPU load; runs comfortably on CPU with 32 GB RAM. |
 | Q5_K_M / Q6_K | 32 GB VRAM, or 48 GB system RAM. |
 | Q8_0 | 48 GB VRAM, or 64 GB system RAM. |
 | BF16 | 80 GB+ VRAM, or 96 GB system RAM. Reference only. |

 | File | Quant | Size | Notes |
 |---|---|---:|---|
+| `Qwen3.8-35B-A3B-IQ2_M.gguf` | IQ2_M | 12.558 GB | Smallest usable. Fits a 16 GB card. |
+| `Qwen3.8-35B-A3B-Q2_K.gguf` | Q2_K | 13.839 GB | 2-bit K-quant; widest runtime support at this size. |
+| `Qwen3.8-35B-A3B-IQ3_M.gguf` | IQ3_M | 16.340 GB | Strong quality per byte at 3-bit. |
+| `Qwen3.8-35B-A3B-Q3_K_M.gguf` | Q3_K_M | 17.664 GB | Conventional 3-bit K-quant. |
+| `Qwen3.8-35B-A3B-IQ4_XS.gguf` | IQ4_XS | 19.628 GB | Near Q4_K_M quality, ~2 GB smaller. |
 | `Qwen3.8-35B-A3B-Q4_K_M.gguf` | Q4_K_M | 21.713 GB | **Recommended.** Best quality/size balance for most users. |
 | `Qwen3.8-35B-A3B-Q5_K_M.gguf` | Q5_K_M | 25.348 GB | Higher quality, modest size increase. |
 | `Qwen3.8-35B-A3B-Q6_K.gguf` | Q6_K | 29.209 GB | Near-lossless. |
 | `Qwen3.8-35B-A3B-BF16.gguf` | BF16 | 71.067 GB | Full precision reference. |
 | `mmproj-Qwen3.8-35B-A3B-F16.gguf` | F16 | 0.899 GB | Vision projector. Pair with any text quant above for image input. |
+The IQ\* quants and the 2/3-bit K-quants are calibrated with an importance matrix, which is what keeps them coherent at these bit-widths.
 Sizes are exact decimal GB from the uploaded files (1 GB = 1,000,000,000 bytes).
 ### What fits?
 | Quant | Guidance |
 |---|---|
+| IQ2_M / Q2_K | 16 GB VRAM, or 16 GB system RAM. The smallest that stay coherent. |
+| IQ3_M / Q3_K_M | 20-24 GB VRAM, or 24 GB system RAM. |
+| IQ4_XS / Q4_K_M | 24 GB VRAM for a full GPU load; comfortable on CPU with 32 GB RAM. |
 | Q5_K_M / Q6_K | 32 GB VRAM, or 48 GB system RAM. |
 | Q8_0 | 48 GB VRAM, or 64 GB system RAM. |
 | BF16 | 80 GB+ VRAM, or 96 GB system RAM. Reference only. |

SHA256SUMS CHANGED Viewed

@@ -1,6 +1,11 @@
-d5ff4a315a370b7b2ddaa7d0a756605f16daf8358f9e6173db08705f1fe0bb4f  Qwen3.8-35B-A3B-BF16.gguf
 196103269085bc54c9b8f49ed21e9f53e1b56b465e8b796c6d8e31e06f63cfa5  Qwen3.8-35B-A3B-Q4_K_M.gguf
 f1903bac4ee3eec1f9013735298867c96d97dfb55c71f826a714b17214abc4ad  Qwen3.8-35B-A3B-Q5_K_M.gguf
 0bb743311ee5d58eeeeff67d42d0e62a52347539e825ceb320d1a2178003f47b  Qwen3.8-35B-A3B-Q6_K.gguf
 7d986d310e686a91cb514cdd819719b4f80ace899c9aaad7bfccfdf4670a9bb3  Qwen3.8-35B-A3B-Q8_0.gguf
 4381cb5110074396c2c7b39221fffae0c31886aa95b674de8e103d97edf58b94  mmproj-Qwen3.8-35B-A3B-F16.gguf

+9c095175f7af0c4acc18e552f0dd7ac8180d81f9ac4bf38d62a03c76a0d6e084  Qwen3.8-35B-A3B-IQ2_M.gguf
+a738c481e78099626bc43a9d4e5e0478266ab7bab5a788225da61310a1dd24dc  Qwen3.8-35B-A3B-Q2_K.gguf
+0ad7b253ecee6de7b38ecaf82ea5553d302c3f924961b7091b7088cc99a30f95  Qwen3.8-35B-A3B-IQ3_M.gguf
+48a5197d37318de7984d0e1d38c66ae00ac5b873ea0920242a1889cc2d35c4a9  Qwen3.8-35B-A3B-Q3_K_M.gguf
+b645af45431ef9b41f43cae51c9323b2d0ca84f23031d483d2105c643ae58d65  Qwen3.8-35B-A3B-IQ4_XS.gguf
 196103269085bc54c9b8f49ed21e9f53e1b56b465e8b796c6d8e31e06f63cfa5  Qwen3.8-35B-A3B-Q4_K_M.gguf
 f1903bac4ee3eec1f9013735298867c96d97dfb55c71f826a714b17214abc4ad  Qwen3.8-35B-A3B-Q5_K_M.gguf
 0bb743311ee5d58eeeeff67d42d0e62a52347539e825ceb320d1a2178003f47b  Qwen3.8-35B-A3B-Q6_K.gguf
 7d986d310e686a91cb514cdd819719b4f80ace899c9aaad7bfccfdf4670a9bb3  Qwen3.8-35B-A3B-Q8_0.gguf
+d5ff4a315a370b7b2ddaa7d0a756605f16daf8358f9e6173db08705f1fe0bb4f  Qwen3.8-35B-A3B-BF16.gguf
 4381cb5110074396c2c7b39221fffae0c31886aa95b674de8e103d97edf58b94  mmproj-Qwen3.8-35B-A3B-F16.gguf