Instructions to use ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-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 ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-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 ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS
# Run inference directly in the terminal:
llama cli -hf ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS
# Run inference directly in the terminal:
llama cli -hf ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

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 ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS
# Run inference directly in the terminal:
./llama-cli -hf ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

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 ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

Use Docker

docker model run hf.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

LM Studio
Jan

vLLM

How to use ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-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": "ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

Ollama
How to use ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF with Ollama:
```
ollama run hf.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS
```
Unsloth Desktop

How to use ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

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": "ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF with Docker Model Runner:
```
docker model run hf.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS
```

Lemonade

How to use ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-GSQ-RCO-GGUF-IQ2_XS

List all available models

lemonade list

Hermes Agent

How to use ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-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 ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

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 ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

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 "ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS" \
  --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"

helcig commited on 27 days ago

Commit

8912338

verified ·

1 Parent(s): 8f752f8

Add GSQ-RCO IQ2_XS (2.50 bpw) + tensor allocation

Browse files

Files changed (4) hide show

.gitattributes +2 -0
IQ2_XS/Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS-00001-of-00002.gguf +3 -0
IQ2_XS/Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS-00002-of-00002.gguf +3 -0
tensor-allocation/Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS-00001-of-00002.rco-allocation.txt +1235 -0

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tensor-allocation/Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS-00001-of-00002.rco-allocation.txt ADDED Viewed

	@@ -0,0 +1,1235 @@

+# RCO per-tensor GGUF allocation
+# Repository: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF
+# GGUF: Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS-00001-of-00002.gguf
+# Source: https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF/resolve/main/Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS-00001-of-00002.gguf
+# Target whole-file bpw: 2.50
+# GGUF version: 3
+# GGUF metadata entries: 73
+# Tensor count: 1223
+# Quant-type counts: BF16=484, F16=1, F32=292, IQ1_M=6, IQ2_S=68, IQ2_XXS=22, IQ3_S=44, IQ4_NL=36, IQ4_XS=181, Q2_0=53, Q6_K=29, Q8_0=7
+# Format: <GGUF tensor name>: <GGML quantization/storage type>
+# Includes fixed non-RCO tensors (e.g. F32/BF16) so the released GGUF is fully reproducible.
+#
+output.weight: IQ4_XS
+output_hc_down.weight: BF16
+output_hc_norm.weight: F32
+output_hc_up.weight: BF16
+token_embd.weight: IQ4_XS
+blk.0.attn_gate.weight: IQ3_S
+blk.0.attn_qkv.weight: IQ4_XS
+blk.0.ffn_down_exps.weight: Q2_0
+blk.0.ffn_down_shexp.weight: IQ4_NL
+blk.0.ffn_gate_exps.weight: IQ2_S
+blk.0.ffn_gate_inp.weight: BF16
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+blk.0.ffn_gate_shexp.weight: IQ3_S
+blk.0.ffn_up_exps.weight: IQ2_S
+blk.0.ffn_up_shexp.weight: IQ4_XS
+blk.0.hc_attn_down.weight: BF16
+blk.0.hc_attn_inject.weight: BF16
+blk.0.hc_attn_norm.weight: F32
+blk.0.hc_attn_up.weight: BF16
+blk.0.hc_ffn_down.weight: BF16
+blk.0.hc_ffn_inject.weight: BF16
+blk.0.hc_ffn_norm.weight: F32
+blk.0.hc_ffn_up.weight: BF16
+blk.0.ssm_a: F32
+blk.0.ssm_alpha.weight: BF16
+blk.0.ssm_beta.weight: BF16
+blk.0.ssm_conv1d.weight: F32
+blk.0.ssm_dt.bias: F32
+blk.0.ssm_norm.weight: F32
+blk.0.ssm_out.weight: IQ4_XS
+blk.1.attn_gate.weight: IQ3_S
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+blk.1.ffn_down_exps.weight: Q2_0
+blk.1.ffn_down_shexp.weight: IQ4_NL
+blk.1.ffn_gate_exps.weight: IQ2_XXS
+blk.1.ffn_gate_inp.weight: BF16
+blk.1.ffn_gate_inp_shexp.weight: BF16
+blk.1.ffn_gate_shexp.weight: Q6_K
+blk.1.ffn_up_exps.weight: IQ2_XXS
+blk.1.ffn_up_shexp.weight: IQ4_XS
+blk.1.hc_attn_down.weight: BF16
+blk.1.hc_attn_inject.weight: BF16
+blk.1.hc_attn_norm.weight: F32
+blk.1.hc_attn_up.weight: BF16
+blk.1.hc_ffn_down.weight: BF16
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+blk.1.hc_ffn_norm.weight: F32
+blk.1.hc_ffn_up.weight: BF16
+blk.1.ple_conv1d.weight: F16
+blk.1.ple_key.weight: BF16
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+blk.1.ple_norm_query.weight: F32
+blk.1.ple_value.weight: BF16
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+blk.1.ssm_norm.weight: F32
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+blk.2.ssm_beta.weight: BF16
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+blk.2.ssm_norm.weight: F32
+blk.2.ssm_out.weight: IQ4_XS
+blk.3.attn_k.weight: Q6_K
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+blk.3.attn_output.weight: IQ3_S
+blk.3.attn_q.weight: IQ4_XS
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+blk.3.ffn_down_exps.weight: Q2_0
+blk.3.ffn_down_shexp.weight: Q8_0
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+blk.3.ffn_gate_inp.weight: BF16
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+blk.3.ffn_gate_shexp.weight: IQ4_XS
+blk.3.ffn_up_exps.weight: IQ2_S
+blk.3.ffn_up_shexp.weight: IQ4_XS
+blk.3.hc_attn_down.weight: BF16
+blk.3.hc_attn_inject.weight: BF16
+blk.3.hc_attn_norm.weight: F32
+blk.3.hc_attn_up.weight: BF16
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+blk.3.hc_ffn_inject.weight: BF16
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+blk.6.ssm_beta.weight: BF16
+blk.6.ssm_conv1d.weight: F32
+blk.6.ssm_dt.bias: F32
+blk.6.ssm_norm.weight: F32
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+blk.7.attn_q_norm.weight: F32
+blk.7.attn_v.weight: Q6_K
+blk.7.ffn_down_exps.weight: Q2_0
+blk.7.ffn_down_shexp.weight: Q2_0
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+blk.7.ffn_gate_inp.weight: BF16
+blk.7.ffn_gate_inp_shexp.weight: BF16
+blk.7.ffn_gate_shexp.weight: Q6_K
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+blk.7.hc_attn_inject.weight: BF16
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+blk.7.hc_ffn_inject.weight: BF16
+blk.7.hc_ffn_norm.weight: F32
+blk.7.hc_ffn_up.weight: BF16
+blk.7.indexer.k_norm.weight: F32
+blk.7.indexer.k_proj.weight: BF16
+blk.7.indexer.q_norm.weight: F32
+blk.7.indexer.q_proj.weight: BF16
+blk.8.attn_gate.weight: IQ4_XS
+blk.8.attn_qkv.weight: IQ4_XS
+blk.8.ffn_down_exps.weight: Q2_0
+blk.8.ffn_down_shexp.weight: IQ4_NL
+blk.8.ffn_gate_exps.weight: IQ1_M
+blk.8.ffn_gate_inp.weight: BF16
+blk.8.ffn_gate_inp_shexp.weight: BF16
+blk.8.ffn_gate_shexp.weight: IQ4_XS
+blk.8.ffn_up_exps.weight: IQ1_M
+blk.8.ffn_up_shexp.weight: IQ4_XS
+blk.8.hc_attn_down.weight: BF16
+blk.8.hc_attn_inject.weight: BF16
+blk.8.hc_attn_norm.weight: F32
+blk.8.hc_attn_up.weight: BF16
+blk.8.hc_ffn_down.weight: BF16
+blk.8.hc_ffn_inject.weight: BF16
+blk.8.hc_ffn_norm.weight: F32
+blk.8.hc_ffn_up.weight: BF16
+blk.8.ssm_a: F32
+blk.8.ssm_alpha.weight: BF16
+blk.8.ssm_beta.weight: BF16
+blk.8.ssm_conv1d.weight: F32
+blk.8.ssm_dt.bias: F32
+blk.8.ssm_norm.weight: F32
+blk.8.ssm_out.weight: IQ3_S
+blk.9.attn_gate.weight: IQ4_XS
+blk.9.attn_qkv.weight: IQ4_XS
+blk.9.ffn_down_exps.weight: Q2_0
+blk.9.ffn_down_shexp.weight: Q8_0
+blk.9.ffn_gate_exps.weight: IQ2_XXS
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