Image-Text-to-Text
GGUF
llama.cpp
qwen
qwen3.8
qwen3.8-flash-next
amd
rocm
gfx1151
ryzen-ai-max-395
strix-halo
mixture-of-experts
iu4
mtp
speculative-decoding
nvme
ple
long-context
local-inference
vision
conversational
Instructions to use jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4 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 jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0 # Run inference directly in the terminal: llama cli -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0 # Run inference directly in the terminal: llama cli -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4: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 jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4: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 jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0
Use Docker
docker model run hf.co/jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0
- LM Studio
- Jan
- vLLM
How to use jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4", "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/jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0
- Ollama
How to use jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4 with Ollama:
ollama run hf.co/jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0
- Unsloth Desktop
- Pi
How to use jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0
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": "jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4 with Docker Model Runner:
docker model run hf.co/jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0
- Lemonade
How to use jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0
Run and chat with the model
lemonade run user.Qwen3.8-Flash-CIRU-STRIX-IU4-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4: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 jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4: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 "jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4: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"
Document two-slot serving and unified KV validation; preserve v2.0 runtime
Browse files- README.md +19 -1
- v2.0-checksums.sha256 +1 -1
README.md
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./scripts/ciru/setup-linux-amd.sh --install-host-deps
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```
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The original archive and Hugging Face `v2.0` revision remain unchanged. Later documentation clarifications appear on `main`;
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Benchmarked on NixOS with ROCm10. The runtime also passed an Ubuntu 24.04 container build and GPU smoke test.
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`min_p=0` explicitly follows [Qwen's model-specific recommendation](https://huggingface.co/Qwen/Qwen3.8-Flash-Next/blob/f5d08274bafd880402bd16f5e3e6c514136ec06c/README.md#best-practices); v1.1 could inherit llama.cpp's `0.05`. This disables an additional probability filter and can change sampled output. We have not established that either value gives better answer quality in a controlled A/B test. Set `MIN_P=0.05` with the launcher to select the previous inherited value; request-level settings can override the server default.
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## Historical task-quality results on the unchanged weights
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These earlier H96/depth-1 results were **not rerun as full suites on v2.0**. The current release evidence comprises the bounded BF16 panel, controlled coding probe, context sweeps, and scoped runtime correctness/build checks described above.
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./scripts/ciru/setup-linux-amd.sh --install-host-deps
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```
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The original archive and Hugging Face `v2.0` revision remain unchanged. Later documentation clarifications and an early multi-slot MTP launcher check appear on GitHub `main`; the released inference code is unchanged. Use the [current running instructions](https://github.com/ciru-ai/Qwen3.8-Flash-CIRU-STRIX-IU4/blob/main/docs/RUNNING.md) for profile verification and draft-depth guidance.
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Benchmarked on NixOS with ROCm10. The runtime also passed an Ubuntu 24.04 container build and GPU smoke test.
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`min_p=0` explicitly follows [Qwen's model-specific recommendation](https://huggingface.co/Qwen/Qwen3.8-Flash-Next/blob/f5d08274bafd880402bd16f5e3e6c514136ec06c/README.md#best-practices); v1.1 could inherit llama.cpp's `0.05`. This disables an additional probability filter and can change sampled output. We have not established that either value gives better answer quality in a controlled A/B test. Set `MIN_P=0.05` with the launcher to select the previous inherited value; request-level settings can override the server default.
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### Parallel agents and unified KV
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**The released MTP shortlist supports one slot only.** For two slots, disable MTP explicitly; otherwise the original v2.0 runtime asserts during initialization. The current GitHub `main` launcher now catches this before loading the model. The original `v2.0` tag and archive remain unchanged.
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From either the original v2.0 runtime directory or the current repository:
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```bash
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ENABLE_MTP=0 PARALLEL_SLOTS=2 CONTEXT_SIZE=524288 \
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BUILD_DIR="$PWD/build-gfx1151-sdk" MODEL_DIR=/absolute/path/to/model \
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./scripts/ciru/run-server.sh --no-kv-unified
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```
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With separate KV caches, `524288` is the total context allocation: **262144 per slot**, not 512K per agent. Inspect `/slots` for the actual limits.
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The released CIRU/ROCm 10 runtime passed 16 target-only requests in our 2026-09-06 two-slot smoke: eight with separate KV and eight with unified KV, including overlapping short/long and streamed requests. This does not validate filled 512K contexts or every agent workload. The older upstream fragmented-KV fix is already included. A separate proposed HIP host-buffer patch is being held because it changed the MTP coding continuation during regression testing; this update does not claim that HIP issue is fixed.
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[Two-slot instructions and upstream issue links](https://github.com/ciru-ai/Qwen3.8-Flash-CIRU-STRIX-IU4/blob/main/docs/RUNNING.md#parallel-requests-and-unified-kv-cache) 路 [Validation report](https://github.com/ciru-ai/Qwen3.8-Flash-CIRU-STRIX-IU4/blob/main/docs/PARALLEL_VALIDATION.md)
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## Historical task-quality results on the unchanged weights
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These earlier H96/depth-1 results were **not rerun as full suites on v2.0**. The current release evidence comprises the bounded BF16 panel, controlled coding probe, context sweeps, and scoped runtime correctness/build checks described above.
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v2.0-checksums.sha256
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f369a1ea3134d72f96dbd9290752fb860334aea20d961eaeb761732bff4be0aa README.md
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