Text Generation
GGUF
rocmfp4
llama.cpp
strix-halo
gfx1151
rocm
amd
ryzen-ai-max
uncensored
research
conversational
Instructions to use kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF 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 kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-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 kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_0 # Run inference directly in the terminal: llama cli -hf kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_0 # Run inference directly in the terminal: llama cli -hf kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_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 kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_0 # Run inference directly in the terminal: ./llama-cli -hf kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_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 kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_0
Use Docker
docker model run hf.co/kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_0
- LM Studio
- Jan
- vLLM
How to use kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-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": "kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_0
- Ollama
How to use kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF with Ollama:
ollama run hf.co/kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_0
- Unsloth Desktop
- Pi
How to use kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_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": "kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF with Docker Model Runner:
docker model run hf.co/kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_0
- Lemonade
How to use kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_0
Run and chat with the model
lemonade run user.Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF-Q4_0
List all available models
lemonade list
- Hermes Agent
How to use kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-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 kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_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 kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_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 "kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF:Q4_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"
Download qwen4exp-qsa-checkpoint-fix.patch from kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 4.94 kB
-
https://huggingface.co/kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF/resolve/main/qwen4exp-qsa-checkpoint-fix.patch
- Command line
-
hf download hf://kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF/qwen4exp-qsa-checkpoint-fix.patch
-
curl -L -o qwen4exp-qsa-checkpoint-fix.patch https://huggingface.co/kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-FAST-GGUF/resolve/main/qwen4exp-qsa-checkpoint-fix.patch
4.94 kB
| From 85d8f7e83499e434c24993e6e1f3800568b5adfd Mon Sep 17 00:00:00 2001 | |
| From: kingjones30 <myron@deploy365.us> | |
| Date: Thu, 3 Sep 2026 12:33:12 +0000 | |
| Subject: [PATCH] fix(qwen4exp): serialize QSA indexer cache in context | |
| checkpoints | |
| llama_memory_hybrid_idx inherited state_write/state_read from the hybrid | |
| base and never overrode them, so checkpoints omitted mem_idx. A restore | |
| then left the QSA index pointing at a different position than the KV | |
| caches; the next kernel could wedge the GPU SDMA queue (field report: | |
| kingjones777/Qwen3.8-Flash-Next-ROCmFP4-STRIX-GGUF#6, @liusecret). | |
| Also treat empty seq_rm ranges as no-ops and honor mem_idx->seq_rm. | |
| --- | |
| common/common.cpp | 16 ++++++++++++++++ | |
| src/llama-memory-hybrid-idx.cpp | 34 ++++++++++++++++++++++++++++++++- | |
| src/llama-memory-hybrid-idx.h | 3 +++ | |
| 3 files changed, 52 insertions(+), 1 deletion(-) | |
| diff --git a/common/common.cpp b/common/common.cpp | |
| index 1421040..e64a71b 100644 | |
| --- a/common/common.cpp | |
| +++ b/common/common.cpp | |
| done: | |
| void common_context_seq_rm(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) { | |
| auto * mem = llama_get_memory(ctx); | |
| + if (mem == nullptr) { | |
| + return; | |
| + } | |
| + // empty range is a no-op, not a fatal. recurrent caches refuse p0 == n_past, | |
| + // p1 == -1 instead of succeeding, which used to abort the server. | |
| + const llama_pos p0n = p0 < 0 ? 0 : p0; | |
| + if (p1 >= 0) { | |
| + if (p0n >= p1) { | |
| + return; | |
| + } | |
| + } else { | |
| + const llama_pos p_max = llama_memory_seq_pos_max(mem, seq_id); | |
| + if (p_max >= 0 && p0n > p_max) { | |
| + return; | |
| + } | |
| + } | |
| if (!llama_memory_seq_rm(mem, seq_id, p0, p1)) { | |
| GGML_ABORT("%s", string_format("failed to remove sequence %d with p0=%d, p1=%d\n", seq_id, p0, p1).c_str()); | |
| } | |
| diff --git a/src/llama-memory-hybrid-idx.cpp b/src/llama-memory-hybrid-idx.cpp | |
| index 586b340..b32b1b7 100644 | |
| --- a/src/llama-memory-hybrid-idx.cpp | |
| +++ b/src/llama-memory-hybrid-idx.cpp | |
| void llama_memory_hybrid_idx::clear(bool data) { | |
| } | |
| bool llama_memory_hybrid_idx::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) { | |
| + // empty range is a no-op. the recurrent cache refuses p0 == n_past, p1 == -1 | |
| + // (rollback past n_rs_seq) instead of succeeding, which made common_context_seq_rm abort. | |
| + { | |
| + const llama_pos p0n = p0 < 0 ? 0 : p0; | |
| + const llama_pos p_max = llama_memory_hybrid::seq_pos_max(seq_id); | |
| + if (p1 >= 0 ? p0n >= p1 : (p_max >= 0 && p0n > p_max)) { | |
| + return true; | |
| + } | |
| + } | |
| + | |
| // same order as llama_memory_hybrid::seq_rm: try the recurrent cache first since it is the | |
| // one that may refuse, and if it does the caches are left untouched | |
| if (!get_mem_recr()->seq_rm(seq_id, p0, p1)) { | |
| bool llama_memory_hybrid_idx::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_po | |
| } | |
| if (mem_idx) { | |
| - mem_idx->seq_rm(seq_id, p0, p1); | |
| + if (!mem_idx->seq_rm(seq_id, p0, p1)) { | |
| + return false; | |
| + } | |
| } | |
| return get_mem_attn()->seq_rm(seq_id, p0, p1); | |
| std::map<ggml_backend_buffer_type_t, size_t> llama_memory_hybrid_idx::memory_bre | |
| return mb; | |
| } | |
| +void llama_memory_hybrid_idx::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const { | |
| + llama_memory_hybrid::state_write(io, seq_id, flags); | |
| + | |
| + // mem_idx is a KV cache — same PARTIAL_ONLY rule as mem_attn in the base. | |
| + // without this, context checkpoints restore attn+recr and silently drop the QSA | |
| + // indexer. after a restore the index describes a different position than the KV | |
| + // caches, and a later kernel can wedge the GPU SDMA queue. | |
| + if (mem_idx && (flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY) == 0) { | |
| + mem_idx->state_write(io, seq_id, flags); | |
| + } | |
| +} | |
| + | |
| +void llama_memory_hybrid_idx::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) { | |
| + llama_memory_hybrid::state_read(io, seq_id, flags); | |
| + | |
| + if (mem_idx && (flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY) == 0) { | |
| + mem_idx->state_read(io, seq_id, flags); | |
| + } | |
| +} | |
| + | |
| llama_kv_cache * llama_memory_hybrid_idx::get_mem_idx() const { | |
| return mem_idx.get(); | |
| } | |
| diff --git a/src/llama-memory-hybrid-idx.h b/src/llama-memory-hybrid-idx.h | |
| index d5e75ef..18a64c4 100644 | |
| --- a/src/llama-memory-hybrid-idx.h | |
| +++ b/src/llama-memory-hybrid-idx.h | |
| public: | |
| std::map<ggml_backend_buffer_type_t, size_t> memory_breakdown() const override; | |
| + void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override; | |
| + void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override; | |
| + | |
| // | |
| // llama_memory_hybrid_idx specific API | |
| // | |
| -- | |
| 2.43.0 | |