Text Generation
GGUF
llama.cpp
qwen3.8
qwen
27b
amd
rocm
gfx1151
strix-halo
iu4
w4a4
kairic-edge
promptforge
dualview
mtp
local-inference
conversational
Instructions to use jcbtc/Qwen3.8-27B-IU4-Kairic-Edge 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/Qwen3.8-27B-IU4-Kairic-Edge 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/Qwen3.8-27B-IU4-Kairic-Edge # Run inference directly in the terminal: llama cli -hf jcbtc/Qwen3.8-27B-IU4-Kairic-Edge
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf jcbtc/Qwen3.8-27B-IU4-Kairic-Edge # Run inference directly in the terminal: llama cli -hf jcbtc/Qwen3.8-27B-IU4-Kairic-Edge
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/Qwen3.8-27B-IU4-Kairic-Edge # Run inference directly in the terminal: ./llama-cli -hf jcbtc/Qwen3.8-27B-IU4-Kairic-Edge
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/Qwen3.8-27B-IU4-Kairic-Edge # Run inference directly in the terminal: ./build/bin/llama-cli -hf jcbtc/Qwen3.8-27B-IU4-Kairic-Edge
Use Docker
docker model run hf.co/jcbtc/Qwen3.8-27B-IU4-Kairic-Edge
- LM Studio
- Jan
- vLLM
How to use jcbtc/Qwen3.8-27B-IU4-Kairic-Edge with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jcbtc/Qwen3.8-27B-IU4-Kairic-Edge" # 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/Qwen3.8-27B-IU4-Kairic-Edge", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/jcbtc/Qwen3.8-27B-IU4-Kairic-Edge
- Ollama
How to use jcbtc/Qwen3.8-27B-IU4-Kairic-Edge with Ollama:
ollama run hf.co/jcbtc/Qwen3.8-27B-IU4-Kairic-Edge
- Unsloth Desktop
- Pi
How to use jcbtc/Qwen3.8-27B-IU4-Kairic-Edge with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf jcbtc/Qwen3.8-27B-IU4-Kairic-Edge
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/Qwen3.8-27B-IU4-Kairic-Edge" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use jcbtc/Qwen3.8-27B-IU4-Kairic-Edge with Docker Model Runner:
docker model run hf.co/jcbtc/Qwen3.8-27B-IU4-Kairic-Edge
- Lemonade
How to use jcbtc/Qwen3.8-27B-IU4-Kairic-Edge with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jcbtc/Qwen3.8-27B-IU4-Kairic-Edge
Run and chat with the model
lemonade run user.Qwen3.8-27B-IU4-Kairic-Edge-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use jcbtc/Qwen3.8-27B-IU4-Kairic-Edge 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/Qwen3.8-27B-IU4-Kairic-Edge
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/Qwen3.8-27B-IU4-Kairic-Edge
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use jcbtc/Qwen3.8-27B-IU4-Kairic-Edge with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf jcbtc/Qwen3.8-27B-IU4-Kairic-Edge
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/Qwen3.8-27B-IU4-Kairic-Edge" \ --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"
Release Kairic Edge runtime v1.2 correctness update
Browse files- README.md +24 -8
- RELEASE_NOTES_v1.2.md +46 -0
README.md
CHANGED
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Kairic Edge combines **Prompt Forge**, **Dual View**, and the accelerated IU4 lane. It connects selected 4-bit activations and weights to AMD's native unsigned/signed 4-bit matrix instruction for prompt and multi-token verification shapes. The important result is not simply a smaller model: it is a working, end-to-end native IU4 compute route inside a served 27B language model.
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> **Custom runtime required.** Standard llama.cpp does not understand the Kairic sidecars or `--kairic-edge`. Build and run the immutable [Kairic Edge v1 source release](https://github.com/ciru-ai/ROCmFPX/tree/kairic-edge-qwen38-27b-v1). The bundled runner enables Kairic Edge, prompt caching, the qualified 256K configuration, and native MTP4 by default.
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## Release highlights
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- **47.73 generated tokens/s** across the 164-task coding suite, **85.0% above Unsloth Dynamic Q4** and **88.6% above Unsloth Dynamic Q6** in the collected configured-system runs.
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- **158/164 HumanEval Base** and **152/164 HumanEval Plus**: Base tied the Q4 comparison and Plus passed four more tasks; it also passed one more Base and two more Plus tasks than the Q6 comparison.
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- The native IU4 instruction harness reached **104.66 TOPS**, or **1.94×** the matched FP16 matrix result and **1.93×** the matched IU8 result on the same device and run.
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## Coding evaluation and throughput
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The table reports our collected 164-task, chat-adapted HumanEval/EvalPlus harness. Every arm ran on the same AMD Ryzen AI Max+ 395 / Radeon 8060S `gfx1151` host with one slot, F16 target/draft KV, batch 2048, ubatch 512, 16 target threads, 32 batch threads, native MTP4, deterministic sampling, one trajectory per task, and no repair retry.
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| Release | Base | Plus | Aggregate PP | Aggregate TG | Peak TG | Generation time |
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|---|---:|---:|---:|---:|---:|---:|
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| **Kairic Edge IU4** | **158/164 (96.34%)** | **152/164 (92.68%)** | **358.45 tok/s** | **47.73 tok/s** | **106.68 tok/s** | **950.45 s** |
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| Unsloth Dynamic Q4 | 158/164 (96.34%) | 148/164 (90.24%) | 314.14 tok/s | 25.80 tok/s | 30.00 tok/s | 1,778.27 s |
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| Unsloth Dynamic Q6 | 157/164 (95.73%) | 150/164 (91.46%) | 260.29 tok/s | 25.31 tok/s | 27.99 tok/s | 1,732.38 s |
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These are **configured-system comparisons**, not a one-variable quantization experiment. Kairic Edge used its release configuration at 262,144 context with an 8 GiB prompt cache and 32 context checkpoints; the comparison runs used 65,536 context without that cache allocation. Generation throughput is the most useful cross-run signal, but the different complete configurations must remain visible. This chat-adapted harness is also not directly comparable to a published EvalPlus leaderboard score.
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The 164-task Kairic run was collected
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### Comparison artifact identity
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### Served verification A/B
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### Generation-throughput peaks
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| Sweep | Aggregate / hot TG | Peak TG | Output gate |
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| 164-task coding suite | **47.73** | **106.68** | 158 Base / 152 Plus |
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| Repeated-prefix cold sweep | 29.98 pooled | **54.00** | fixed 128-token generations |
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| Repeated-prefix warm-cache sweep | — | **99.41** | single 8K point |
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| Release-runner qualification | **123.19** | — | byte-identical 512-token output to predecessor |
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git clone https://github.com/ciru-ai/ROCmFPX.git
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cd ROCmFPX
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git checkout kairic-edge-qwen38-27b-v1.
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git clone https://github.com/ROCm/composable_kernel.git third_party/composable_kernel
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git -C third_party/composable_kernel checkout fdf4bb7fcc984811cef48ce817d89aac064b984a
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./build-kairic/bin/llama-server --help | grep -A1 -- '--kairic-edge'
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```
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See the release repository's [build and verification guide](https://github.com/ciru-ai/ROCmFPX/blob/kairic-edge-qwen38-27b-v1.
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## Recommended launch
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context checkpoints 32
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Kairic Edge enabled
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native MTP depth 4, explicit
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sampler temperature 0, top-p 1, top-k 0, min-p 0
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reasoning off
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metrics enabled
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- Full-suite comparisons are configured-system measurements with different context/cache allocations. They are not proof that quantization alone caused every delta.
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- One slot was qualified. Multi-user concurrency, sustained power, and cross-vendor performance remain future work.
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- Prompt caching needs context checkpoints for correct recurrent-state restoration. Keep `-ctxcp 32` with the recommended runner.
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- The release preserves strong task results, but no finite benchmark establishes universal quality equivalence to BF16.
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## Model lineage, license, and credits
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Kairic Edge combines **Prompt Forge**, **Dual View**, and the accelerated IU4 lane. It connects selected 4-bit activations and weights to AMD's native unsigned/signed 4-bit matrix instruction for prompt and multi-token verification shapes. The important result is not simply a smaller model: it is a working, end-to-end native IU4 compute route inside a served 27B language model.
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> **Custom runtime required.** Standard llama.cpp does not understand the Kairic sidecars or `--kairic-edge`. Build and run the immutable [Kairic Edge v1.2 source release](https://github.com/ciru-ai/ROCmFPX/tree/kairic-edge-qwen38-27b-v1.2). The bundled runner enables Kairic Edge, prompt caching, the qualified 256K configuration, and native MTP4 by default.
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## v1.2 correctness update
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Kairic Edge v1.2 keeps 24/64/64 n-gram drafting but changes exact 65-row verification to the compact authoritative path. The earlier native IU4 M65 verifier could select a different greedy token from M1/no-spec decoding on a reproduced low-margin case. Speculative decoding must change speed, not the target model's answer, so the native M65 specialization is no longer a production default.
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On the frozen 1,967-token structured-generation repro, v1.2 produced the exact target response hash in all six runs. Five warm runs completed in **22.31–22.38 seconds** (mean **22.34 seconds**), accepted **9,255/9,280 drafted tokens (99.73%)**, and retained a mean accepted length of **64.83 tokens**. The strict route measured approximately **5–8% below** the unsafe native M65 verifier while remaining about **7.19× faster** than speculation off on this workload.
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The model and three `.pfs` files are unchanged. v1.2 is a runtime correctness release. PromptForge prefill, M1 target decode, M2–M5 MTP, prompt caching, context size, and API behavior are unchanged.
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Release source: [`kairic-edge-qwen38-27b-v1.2`](https://github.com/ciru-ai/ROCmFPX/tree/kairic-edge-qwen38-27b-v1.2), commit [`205a3e5f40e5542e2f2eb68e3d3f81f918b1d895`](https://github.com/ciru-ai/ROCmFPX/commit/205a3e5f40e5542e2f2eb68e3d3f81f918b1d895). Full details are in [`RELEASE_NOTES_v1.2.md`](https://huggingface.co/jcbtc/Qwen3.8-27B-IU4-Kairic-Edge/blob/main/RELEASE_NOTES_v1.2.md).
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## Release highlights
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- **v1.2 makes exact-M65 verification target-equivalent by default** while retaining 64-token n-gram drafts and 99.73% acceptance on the reproduced stability gate.
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- **47.73 generated tokens/s** across the 164-task coding suite, **85.0% above Unsloth Dynamic Q4** and **88.6% above Unsloth Dynamic Q6** in the collected configured-system runs.
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- **158/164 HumanEval Base** and **152/164 HumanEval Plus**: Base tied the Q4 comparison and Plus passed four more tasks; it also passed one more Base and two more Plus tasks than the Q6 comparison.
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- The native IU4 instruction harness reached **104.66 TOPS**, or **1.94×** the matched FP16 matrix result and **1.93×** the matched IU8 result on the same device and run.
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## Coding evaluation and throughput
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The table reports our collected 164-task, chat-adapted HumanEval/EvalPlus harness from the preceding v1/v1.1 configured system. Every arm ran on the same AMD Ryzen AI Max+ 395 / Radeon 8060S `gfx1151` host with one slot, F16 target/draft KV, batch 2048, ubatch 512, 16 target threads, 32 batch threads, native MTP4, deterministic sampling, one trajectory per task, and no repair retry.
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| Release | Base | Plus | Aggregate PP | Aggregate TG | Peak TG | Generation time |
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|---|---:|---:|---:|---:|---:|---:|
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| **Kairic Edge IU4 v1/v1.1** | **158/164 (96.34%)** | **152/164 (92.68%)** | **358.45 tok/s** | **47.73 tok/s** | **106.68 tok/s** | **950.45 s** |
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| Unsloth Dynamic Q4 | 158/164 (96.34%) | 148/164 (90.24%) | 314.14 tok/s | 25.80 tok/s | 30.00 tok/s | 1,778.27 s |
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| Unsloth Dynamic Q6 | 157/164 (95.73%) | 150/164 (91.46%) | 260.29 tok/s | 25.31 tok/s | 27.99 tok/s | 1,732.38 s |
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These are **configured-system comparisons**, not a one-variable quantization experiment. Kairic Edge used its release configuration at 262,144 context with an 8 GiB prompt cache and 32 context checkpoints; the comparison runs used 65,536 context without that cache allocation. Generation throughput is the most useful cross-run signal, but the different complete configurations must remain visible. This chat-adapted harness is also not directly comparable to a published EvalPlus leaderboard score.
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The 164-task Kairic run was collected before the v1.2 strict-M65 correction and is retained as historical configured-system evidence, not relabeled as a fresh v1.2 speed run. v1.2 preserves the target model's greedy output, but exact-M65-heavy workloads can be approximately 5–8% slower than the unsafe native verifier. The v1.2 release gate instead freezes target equivalence, draft acceptance, and the bounded performance cost on the reproduced failure.
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### Comparison artifact identity
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### Served verification A/B
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The original exact-M65 controlled serving A/B at 65,536 context measured **48.73 tok/s** on compact verification and **52.57 tok/s** on native IU4 verification, a **7.89%** native advantage. A later deterministic trace found that the native path could change a target greedy token despite passing the ten-task screen. v1.2 therefore promotes compact verification as the correctness-preserving default. The old native path is retained only for diagnostics through `KAIRIC_UNSAFE_NATIVE_M65_VERIFY=1`; do not enable it for correctness-sensitive serving.
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The v1.2 six-run gate produced one target-identical response hash. Cold target-reference generation took **161.26 seconds**; five warm strict-M65 rows took **22.31–22.38 seconds**, with **99.73%** draft-token acceptance and mean accepted length **64.83**.
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### Generation-throughput peaks
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| Sweep | Aggregate / hot TG | Peak TG | Output gate |
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|---|---:|---:|---|
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| 164-task coding suite (v1/v1.1) | **47.73** | **106.68** | 158 Base / 152 Plus |
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| Repeated-prefix cold sweep | 29.98 pooled | **54.00** | fixed 128-token generations |
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| Repeated-prefix warm-cache sweep | — | **99.41** | single 8K point |
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| Release-runner qualification | **123.19** | — | byte-identical 512-token output to predecessor |
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git clone https://github.com/ciru-ai/ROCmFPX.git
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cd ROCmFPX
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git checkout kairic-edge-qwen38-27b-v1.2
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git clone https://github.com/ROCm/composable_kernel.git third_party/composable_kernel
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git -C third_party/composable_kernel checkout fdf4bb7fcc984811cef48ce817d89aac064b984a
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./build-kairic/bin/llama-server --help | grep -A1 -- '--kairic-edge'
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```
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See the release repository's [build and verification guide](https://github.com/ciru-ai/ROCmFPX/blob/kairic-edge-qwen38-27b-v1.2/docs/kairic-edge-gfx1151.md) for dependency checks, exact compiler identity, compatibility mode, and smoke tests.
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## Recommended launch
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context checkpoints 32
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Kairic Edge enabled
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native MTP depth 4, explicit
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ngram match/min/max 24/64/64
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M65 verifier strict compact/reference
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sampler temperature 0, top-p 1, top-k 0, min-p 0
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reasoning off
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metrics enabled
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- Full-suite comparisons are configured-system measurements with different context/cache allocations. They are not proof that quantization alone caused every delta.
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- One slot was qualified. Multi-user concurrency, sustained power, and cross-vendor performance remain future work.
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- Prompt caching needs context checkpoints for correct recurrent-state restoration. Keep `-ctxcp 32` with the recommended runner.
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- Exact-M65 native IU4 verification is intentionally disabled in v1.2 because it changed a reproduced greedy output. `KAIRIC_UNSAFE_NATIVE_M65_VERIFY=1` exists only for controlled diagnostics and is not a production setting.
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- The release preserves strong task results, but no finite benchmark establishes universal quality equivalence to BF16.
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## Model lineage, license, and credits
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RELEASE_NOTES_v1.2.md
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# Kairic Edge v1.2
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Kairic Edge v1.2 is a runtime correctness release. The GGUF and all three PromptForge sidecars are unchanged.
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## Fixed
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- Exact 65-row n-gram verification now uses the compact authoritative computation path by default.
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- The previous native IU4 M65 verifier could change a greedy target token on a reproduced low-margin case.
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- Kairic startup now reports `M65 verifier=strict-compact` so the active correctness mode is auditable.
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| 10 |
+
- The previous route remains available only through `KAIRIC_UNSAFE_NATIVE_M65_VERIFY=1` for controlled diagnostics.
|
| 11 |
+
|
| 12 |
+
## Validation
|
| 13 |
+
|
| 14 |
+
- Six out of six responses matched the frozen no-spec target hash exactly.
|
| 15 |
+
- Five warm strict-M65 runs: 22.31–22.38 seconds, mean 22.34 seconds.
|
| 16 |
+
- Draft acceptance: 9,255/9,280 tokens, 99.73%.
|
| 17 |
+
- Mean accepted draft length: 64.83 tokens.
|
| 18 |
+
- Approximately 5–8% slower than the unsafe native M65 route on the reproduced workload.
|
| 19 |
+
- Approximately 7.19× faster than speculation off on the same workload.
|
| 20 |
+
|
| 21 |
+
The frozen target output contains two known arithmetic mistakes. v1.2 intentionally preserves those tokens: speculative decoding is required to accelerate the target model, not silently change its answer.
|
| 22 |
+
|
| 23 |
+
## Release scope
|
| 24 |
+
|
| 25 |
+
The release contains only the M65 correctness change and its documentation. The separately tested Qwen MTP16 verification/adaptive controls were not promoted because the matched MTP4 production profile remained faster. Native MTP4 therefore remains the recommended production default.
|
| 26 |
+
|
| 27 |
+
## Unchanged artifacts
|
| 28 |
+
|
| 29 |
+
| File | SHA-256 |
|
| 30 |
+
|---|---|
|
| 31 |
+
| `Qwen3.8-27B-IU4-Kairic-Edge.gguf` | `360caf7381907c3eca7ac0afd1228efc016af747f3f38637fb1c7f94daabac2a` |
|
| 32 |
+
| `Qwen3.8-27B-Kairic-IU4-FFN.pfs` | `adcbb90a7b429a30a2a39043366d68320d72e8b4816a0f498e882b2f80a2ba2b` |
|
| 33 |
+
| `Qwen3.8-27B-Kairic-IU4-GDN.pfs` | `82f931316f1c895da104915dec4697163808d06f0e6b2dc027cee7aa3afc0f0e` |
|
| 34 |
+
| `Qwen3.8-27B-Kairic-IU4-GDN-Output.pfs` | `3b07e7b176559e4402924ba0c368532fa6f02118a33c71e70974c809bf6208a3` |
|
| 35 |
+
|
| 36 |
+
## Required source release
|
| 37 |
+
|
| 38 |
+
Build tag: [`kairic-edge-qwen38-27b-v1.2`](https://github.com/ciru-ai/ROCmFPX/tree/kairic-edge-qwen38-27b-v1.2) in `ciru-ai/ROCmFPX`.
|
| 39 |
+
|
| 40 |
+
Source commit: [`205a3e5f40e5542e2f2eb68e3d3f81f918b1d895`](https://github.com/ciru-ai/ROCmFPX/commit/205a3e5f40e5542e2f2eb68e3d3f81f918b1d895).
|
| 41 |
+
|
| 42 |
+
The immutable tag exists publicly and the release source passed a clean `llama-server` build, startup/help check, and runtime dependency check on Sozo.
|
| 43 |
+
|
| 44 |
+
## Acknowledgment
|
| 45 |
+
|
| 46 |
+
Pete independently proposed the same fail-closed M65 direction in the Hugging Face discussion. We statically cross-checked that proposal against this implementation before release; v1.2 keeps the stricter explicit unsafe override and auditable startup mode.
|