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"
fix: complete MTP patch + QSA checkpoint fix (2026-09-17)
Browse files
qwen4exp-qsa-checkpoint-fix.patch
ADDED
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@@ -0,0 +1,122 @@
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| 1 |
+
From 85d8f7e83499e434c24993e6e1f3800568b5adfd Mon Sep 17 00:00:00 2001
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| 2 |
+
From: kingjones30 <myron@deploy365.us>
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| 3 |
+
Date: Thu, 3 Sep 2026 12:33:12 +0000
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| 4 |
+
Subject: [PATCH] fix(qwen4exp): serialize QSA indexer cache in context
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| 5 |
+
checkpoints
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| 6 |
+
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| 7 |
+
llama_memory_hybrid_idx inherited state_write/state_read from the hybrid
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| 8 |
+
base and never overrode them, so checkpoints omitted mem_idx. A restore
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| 9 |
+
then left the QSA index pointing at a different position than the KV
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| 10 |
+
caches; the next kernel could wedge the GPU SDMA queue (field report:
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| 11 |
+
kingjones777/Qwen3.8-Flash-Next-ROCmFP4-STRIX-GGUF#6, @liusecret).
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| 12 |
+
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| 13 |
+
Also treat empty seq_rm ranges as no-ops and honor mem_idx->seq_rm.
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| 14 |
+
---
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| 15 |
+
common/common.cpp | 16 ++++++++++++++++
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| 16 |
+
src/llama-memory-hybrid-idx.cpp | 34 ++++++++++++++++++++++++++++++++-
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| 17 |
+
src/llama-memory-hybrid-idx.h | 3 +++
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+
3 files changed, 52 insertions(+), 1 deletion(-)
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| 19 |
+
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| 20 |
+
diff --git a/common/common.cpp b/common/common.cpp
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| 21 |
+
index 1421040..e64a71b 100644
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| 22 |
+
--- a/common/common.cpp
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| 23 |
+
+++ b/common/common.cpp
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| 24 |
+
@@ -1510,6 +1510,22 @@ done:
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| 25 |
+
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| 26 |
+
void common_context_seq_rm(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
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| 27 |
+
auto * mem = llama_get_memory(ctx);
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| 28 |
+
+ if (mem == nullptr) {
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| 29 |
+
+ return;
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| 30 |
+
+ }
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| 31 |
+
+ // empty range is a no-op, not a fatal. recurrent caches refuse p0 == n_past,
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| 32 |
+
+ // p1 == -1 instead of succeeding, which used to abort the server.
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| 33 |
+
+ const llama_pos p0n = p0 < 0 ? 0 : p0;
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| 34 |
+
+ if (p1 >= 0) {
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| 35 |
+
+ if (p0n >= p1) {
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| 36 |
+
+ return;
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| 37 |
+
+ }
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| 38 |
+
+ } else {
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| 39 |
+
+ const llama_pos p_max = llama_memory_seq_pos_max(mem, seq_id);
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| 40 |
+
+ if (p_max >= 0 && p0n > p_max) {
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| 41 |
+
+ return;
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| 42 |
+
+ }
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| 43 |
+
+ }
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| 44 |
+
if (!llama_memory_seq_rm(mem, seq_id, p0, p1)) {
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| 45 |
+
GGML_ABORT("%s", string_format("failed to remove sequence %d with p0=%d, p1=%d\n", seq_id, p0, p1).c_str());
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| 46 |
+
}
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| 47 |
+
diff --git a/src/llama-memory-hybrid-idx.cpp b/src/llama-memory-hybrid-idx.cpp
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| 48 |
+
index 586b340..b32b1b7 100644
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| 49 |
+
--- a/src/llama-memory-hybrid-idx.cpp
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| 50 |
+
+++ b/src/llama-memory-hybrid-idx.cpp
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| 51 |
+
@@ -142,6 +142,16 @@ void llama_memory_hybrid_idx::clear(bool data) {
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| 52 |
+
}
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| 53 |
+
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| 54 |
+
bool llama_memory_hybrid_idx::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
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| 55 |
+
+ // empty range is a no-op. the recurrent cache refuses p0 == n_past, p1 == -1
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| 56 |
+
+ // (rollback past n_rs_seq) instead of succeeding, which made common_context_seq_rm abort.
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| 57 |
+
+ {
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| 58 |
+
+ const llama_pos p0n = p0 < 0 ? 0 : p0;
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| 59 |
+
+ const llama_pos p_max = llama_memory_hybrid::seq_pos_max(seq_id);
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| 60 |
+
+ if (p1 >= 0 ? p0n >= p1 : (p_max >= 0 && p0n > p_max)) {
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| 61 |
+
+ return true;
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| 62 |
+
+ }
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| 63 |
+
+ }
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| 64 |
+
+
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| 65 |
+
// same order as llama_memory_hybrid::seq_rm: try the recurrent cache first since it is the
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| 66 |
+
// one that may refuse, and if it does the caches are left untouched
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| 67 |
+
if (!get_mem_recr()->seq_rm(seq_id, p0, p1)) {
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| 68 |
+
@@ -149,7 +159,9 @@ bool llama_memory_hybrid_idx::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_po
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| 69 |
+
}
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| 70 |
+
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| 71 |
+
if (mem_idx) {
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| 72 |
+
- mem_idx->seq_rm(seq_id, p0, p1);
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| 73 |
+
+ if (!mem_idx->seq_rm(seq_id, p0, p1)) {
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| 74 |
+
+ return false;
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| 75 |
+
+ }
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| 76 |
+
}
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| 77 |
+
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| 78 |
+
return get_mem_attn()->seq_rm(seq_id, p0, p1);
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| 79 |
+
@@ -199,6 +211,26 @@ std::map<ggml_backend_buffer_type_t, size_t> llama_memory_hybrid_idx::memory_bre
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| 80 |
+
return mb;
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| 81 |
+
}
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| 82 |
+
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| 83 |
+
+void llama_memory_hybrid_idx::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
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| 84 |
+
+ llama_memory_hybrid::state_write(io, seq_id, flags);
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| 85 |
+
+
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| 86 |
+
+ // mem_idx is a KV cache — same PARTIAL_ONLY rule as mem_attn in the base.
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| 87 |
+
+ // without this, context checkpoints restore attn+recr and silently drop the QSA
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| 88 |
+
+ // indexer. after a restore the index describes a different position than the KV
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| 89 |
+
+ // caches, and a later kernel can wedge the GPU SDMA queue.
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| 90 |
+
+ if (mem_idx && (flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY) == 0) {
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| 91 |
+
+ mem_idx->state_write(io, seq_id, flags);
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| 92 |
+
+ }
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| 93 |
+
+}
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| 94 |
+
+
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| 95 |
+
+void llama_memory_hybrid_idx::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
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| 96 |
+
+ llama_memory_hybrid::state_read(io, seq_id, flags);
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| 97 |
+
+
|
| 98 |
+
+ if (mem_idx && (flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY) == 0) {
|
| 99 |
+
+ mem_idx->state_read(io, seq_id, flags);
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| 100 |
+
+ }
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| 101 |
+
+}
|
| 102 |
+
+
|
| 103 |
+
llama_kv_cache * llama_memory_hybrid_idx::get_mem_idx() const {
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| 104 |
+
return mem_idx.get();
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| 105 |
+
}
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| 106 |
+
diff --git a/src/llama-memory-hybrid-idx.h b/src/llama-memory-hybrid-idx.h
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| 107 |
+
index d5e75ef..18a64c4 100644
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| 108 |
+
--- a/src/llama-memory-hybrid-idx.h
|
| 109 |
+
+++ b/src/llama-memory-hybrid-idx.h
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| 110 |
+
@@ -75,6 +75,9 @@ public:
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| 111 |
+
|
| 112 |
+
std::map<ggml_backend_buffer_type_t, size_t> memory_breakdown() const override;
|
| 113 |
+
|
| 114 |
+
+ void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override;
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| 115 |
+
+ void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override;
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| 116 |
+
+
|
| 117 |
+
//
|
| 118 |
+
// llama_memory_hybrid_idx specific API
|
| 119 |
+
//
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| 120 |
+
--
|
| 121 |
+
2.43.0
|
| 122 |
+
|