Instructions to use jcbtc/Hy3-Chadrock-FPX-IFP2-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jcbtc/Hy3-Chadrock-FPX-IFP2-MTP with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jcbtc/Hy3-Chadrock-FPX-IFP2-MTP",
	filename="Hy3-Chadrock-FPX-IFP2-MTP-00001-of-00005.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use jcbtc/Hy3-Chadrock-FPX-IFP2-MTP 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/Hy3-Chadrock-FPX-IFP2-MTP
# Run inference directly in the terminal:
llama cli -hf jcbtc/Hy3-Chadrock-FPX-IFP2-MTP

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jcbtc/Hy3-Chadrock-FPX-IFP2-MTP
# Run inference directly in the terminal:
llama cli -hf jcbtc/Hy3-Chadrock-FPX-IFP2-MTP

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/Hy3-Chadrock-FPX-IFP2-MTP
# Run inference directly in the terminal:
./llama-cli -hf jcbtc/Hy3-Chadrock-FPX-IFP2-MTP

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/Hy3-Chadrock-FPX-IFP2-MTP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jcbtc/Hy3-Chadrock-FPX-IFP2-MTP

Use Docker

docker model run hf.co/jcbtc/Hy3-Chadrock-FPX-IFP2-MTP

LM Studio
Jan

vLLM

How to use jcbtc/Hy3-Chadrock-FPX-IFP2-MTP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jcbtc/Hy3-Chadrock-FPX-IFP2-MTP"
# 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/Hy3-Chadrock-FPX-IFP2-MTP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jcbtc/Hy3-Chadrock-FPX-IFP2-MTP

Ollama
How to use jcbtc/Hy3-Chadrock-FPX-IFP2-MTP with Ollama:
```
ollama run hf.co/jcbtc/Hy3-Chadrock-FPX-IFP2-MTP
```

Unsloth Studio

How to use jcbtc/Hy3-Chadrock-FPX-IFP2-MTP with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jcbtc/Hy3-Chadrock-FPX-IFP2-MTP to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jcbtc/Hy3-Chadrock-FPX-IFP2-MTP to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jcbtc/Hy3-Chadrock-FPX-IFP2-MTP to start chatting

How to use jcbtc/Hy3-Chadrock-FPX-IFP2-MTP with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jcbtc/Hy3-Chadrock-FPX-IFP2-MTP

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/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/Hy3-Chadrock-FPX-IFP2-MTP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use jcbtc/Hy3-Chadrock-FPX-IFP2-MTP 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/Hy3-Chadrock-FPX-IFP2-MTP

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/Hy3-Chadrock-FPX-IFP2-MTP

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use jcbtc/Hy3-Chadrock-FPX-IFP2-MTP with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jcbtc/Hy3-Chadrock-FPX-IFP2-MTP

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/Hy3-Chadrock-FPX-IFP2-MTP" \
  --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"

Docker Model Runner
How to use jcbtc/Hy3-Chadrock-FPX-IFP2-MTP with Docker Model Runner:
```
docker model run hf.co/jcbtc/Hy3-Chadrock-FPX-IFP2-MTP
```

Lemonade

How to use jcbtc/Hy3-Chadrock-FPX-IFP2-MTP with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jcbtc/Hy3-Chadrock-FPX-IFP2-MTP

Run and chat with the model

lemonade run user.Hy3-Chadrock-FPX-IFP2-MTP-{{QUANT_TAG}}

List all available models

lemonade list

Hy3 Chadrock FPX-IFP2 MTP

This model requires the Ciru ROCmFPX runner. Stock llama.cpp does not understand the ROCmFPX tensor types and will not run this GGUF correctly. The required ROCmFP2/IFP2 kernels, native Hy3 MTP loader, Hy3 Jinja support, and SSD-backed prompt cache are merged into ciru-ai/ROCmFPX main at commit 647fd52. The upstream portability work is also tracked in charlie12345/ROCmFPX PR #29.

Hy3 Chadrock FPX-IFP2 MTP is a speed-oriented, importance-guided quant of Tencent's Hy3: a 295B-total, 21B-active MoE model with a native 3.8B-parameter MTP layer. It is built for large-memory AMD systems where model traffic, KV capacity, prompt reuse, agent quality, and real end-to-end latency all matter.

The release is 90.761 GiB across five GGUF shards. It fits a tested 64K operational profile with target and MTP-draft KV both at q8_0 on a 128 GiB Strix Halo UMA system. Its disk-only prompt cache keeps reusable state on SSD instead of consuming several additional GiB of scarce system RAM.

The short version: FPX-IFP2 kept the unusually strong HY3 agent/tool behavior, beat its unweighted control on the full Tool-Eval score, improved most paired latency tests, remained smaller than IQ2_M, and avoided the large capacity penalty of FP3-heavy and Q3_K_M alternatives.

Full Tool-Eval69: 88/100, 122/138 points
Tool-Eval core15: 100/100
HermesAgent-08: 100/100
Targeted Hermes set: 425/425, exact quality preservation versus the control
Cache-on HermesAgent-20: 80/100 versus 81/100 for the clean control, treated as variance-level parity
Tested operating point: 64K context, native MTP, q8_0 target/draft KV, SSD prompt cache

For an accessible explanation of the codebook, block layout, scale metadata, int8 reconstruction, AMD dot path, and the research results behind this release, see the ROCmFP2 Codebook Research Report.

What FPX-IFP2 means

FPX-IFP2 is the importance-guided edition of this ROCmFP2 recipe. It is not a claim that every tensor in the model is FP2.

Model region	Quantization choice	Reason
237 trunk routed-expert tensors	ROCmFP2 with importance-guided scale search	Put the compact, fast codebook on the model's large expert body
3 native MTP expert tensors	ROCmFP3	Preserve draft quality and MTP acceptance
Attention and shared experts	Q5_K	Protect high-sensitivity shared computation
Embedding and output	Q6_K	Protect token representation and logits

A ROCmFP2 block holds 32 weights in a 64-bit two-bit payload plus a 16-bit scale: 10 bytes total, or 2.50 bits per weight for those tensors. At runtime, the symbols map to a compact signed-int8 codebook so AMD's integer dot-product path can do the useful accumulation without expanding the whole tensor to floating point first. The complete mixed model projects to about 2.61 BPW, because the sensitive tensors deliberately use more bits.

The importance matrix changes the offline scale choice for each eligible block. It does not enlarge the ten-byte block and it does not add another runtime kernel. Relative to the exact unweighted tensor layout, the stored importance/provenance addition is only 224 bytes.

Why this recipe has a useful place

Footprint and capacity

Artifact	Size	Result
Hy3 Chadrock FPX-IFP2	97,453,897,696 bytes / 90.761 GiB	Release artifact; five shards
IQ2_M comparator	100,008,834,816 bytes / 93.140 GiB	FPX-IFP2 is 2.55% smaller
FP3-heavy quality-v1 experiment	110,382,692,096 bytes / 102.802 GiB	13.27% larger than the FP2-dominant control and needed lower target-KV precision at 64K
Public Q3_K_M artifact	about 142.74 GB	Capacity-excluded from the 128 GiB UMA target before a fair runtime test

Q3_K_M may still be attractive on a machine with more memory, but it is not a substitute for this recipe's capacity target. The FP3-heavy experiment also failed to buy a better frontier: it was larger, slower in the paired agent/tool tests, and scored lower on the selected Hermes set.

Direct ROCmFP2 control versus IQ2_M

The following are paired measurements of the unweighted ROCmFP2 speed-v1 twin against IQ2_M. They isolate the underlying codebook/layout comparison; they are not mislabeled as a direct final-IFP2-versus-IQ2_M run.

Workload	ROCmFP2 control	IQ2_M	ROCmFP2 result
No-MTP short total	9.390 s	9.617 s	2.4% faster
No-MTP long total, 19,654 prompt tokens	159.243 s	169.738 s	6.2% faster
HermesAgent-08	100; 62.446 s at MTP n=2	100; 72.902 s at tuned n=3	Same score, 14.3% faster
Tool-Eval core15	100; 175.3 s	100	Same score, 21.2% less wall time

Raw token generation was 1.5–2.0% lower in those no-MTP trials. The end-to-end win came from lower model traffic and faster prompt processing, which is exactly why a decode-only number does not describe this quant well.

What the importance-guided release added

Against its exact unweighted twin, the final FPX-IFP2 artifact delivered:

Paired test	FPX-IFP2 change
No-MTP short total latency	4.32% faster
No-MTP long total latency	4.83% faster
Short-prompt MTP total latency	3.84% faster
Targeted Hermes duration	2.53% faster
Long-prompt MTP total latency	0.75% slower

On that last long-MTP probe, FPX-IFP2 generation rate fell to 6.521 tok/s from the control's 7.865 tok/s, while MTP acceptance fell to 20/28 (71.43%) from 38/43 (88.37%). Partial-rejection cost dominated this workload. This release is therefore strongest for no-MTP, shorter MTP, and agent/tool traffic; if your workload is dominated by very long continuations, test with MTP disabled as well.

Quality results

All results below were recorded on the same local AMD test system and use the final importance-guided artifact unless a comparator is named.

Evaluation	FPX-IFP2	Comparator / interpretation
HermesAgent-08	100/100	Quality gate passed
Targeted 8-scenario Hermes set	425/425	Exact tie with unweighted control; successful preservation
Tool-Eval core15	100/100	Core tool-use gate passed
Full Tool-Eval69	88/100 (122/138)	Control: 86/100 (119/138)
Cache-on HermesAgent-20	80/100	Clean control: 81/100; variance-level parity, not a failed run
Safety & Boundaries slice	69%	Do not deploy as an unsupervised security-sensitive agent

Full Tool-Eval wall time was 1,885.7 seconds versus 1,697.3 seconds for the control. Two successful IFP2 cases, TC-42 and TC-45, each continued to the 4,096-token cap, so this result supports the score improvement, not a full-suite runtime win. Cache-on HermesAgent-20 completed in 38m 16.652s with aggregate PP 185.42 tok/s and TG 17.07 tok/s over 260,725 prompt and 12,166 predicted tokens.

The FP3-heavy experiment was a negative result: at 102.802 GiB it could not use the 64K q8_0 target-KV goal, tied HA-08 quality while taking 75.306 seconds versus 62.446, scored 405 versus 425 on the targeted Hermes set, and scored 97 versus 100 on Tool-Eval core15 while taking 205.3 versus 175.3 seconds.

Required runner and build

This GGUF contains ROCmFPX types that are outside stock llama.cpp. Clone and pin the required runner before downloading or serving the model:

git clone https://github.com/ciru-ai/ROCmFPX.git
cd ROCmFPX
git checkout main
git pull --ff-only origin main
git checkout 647fd52965a401d7fa8e035fc76a16c94216a794

CMAKE_HIP_ARCHITECTURES=gfx1151 \
BUILD_DIR=build-hy3 \
JOBS=16 \
scripts/build-strix-rocmfp4-mtp.sh llama-server

The script name retains its historical rocmfp4 label, but this build includes the full ROCmFPX/HY3/FPX-IFP2 runtime. gfx1151 is the tested Strix Halo target; choose the matching HIP architecture when building for another supported AMD GPU.

Recommended 64K operational profile

This is a serving default, not a benchmark profile: it has no fixed seed, no forced short generation cap, prompt caching enabled, native MTP enabled, and a conservative one-slot 64K context. Bind remains local by default; put an authenticated gateway in front of it before exposing it to a network.

The commands below are safe to copy and paste without defining either variable first. By default they download under $HOME/models and cache under $HOME/.cache, so they will not accidentally create a made-up /mnt/nvme0 path. To put the prompt cache on a separate SSD, export CACHE_DIR to an existing directory on that SSD before running the block, for example export CACHE_DIR=/your/ssd/hy3-cache.

MODEL_DIR="${MODEL_DIR:-$HOME/models/hy3-chadrock-fpx-ifp2-mtp}"
CACHE_DIR="${CACHE_DIR:-$HOME/.cache/hy3-chadrock-fpx-ifp2-mtp}"

mkdir -p "$MODEL_DIR" "$CACHE_DIR"
test -w "$CACHE_DIR" || { echo "CACHE_DIR is not writable: $CACHE_DIR" >&2; exit 1; }
df -h "$CACHE_DIR"
hf download jcbtc/Hy3-Chadrock-FPX-IFP2-MTP \
  --include '*.gguf' \
  --local-dir "$MODEL_DIR"

Then start the required runner from the ROCmFPX repository:

MODEL_DIR="${MODEL_DIR:-$HOME/models/hy3-chadrock-fpx-ifp2-mtp}"
CACHE_DIR="${CACHE_DIR:-$HOME/.cache/hy3-chadrock-fpx-ifp2-mtp}"

./build-hy3/bin/llama-server \
  -m "$MODEL_DIR/Hy3-Chadrock-FPX-IFP2-MTP-00001-of-00005.gguf" \
  --alias hy3-chadrock-fpx-ifp2-mtp \
  --host 127.0.0.1 --port 8080 \
  -c 65536 -np 1 \
  --device ROCm0 --split-mode none -ngl 999 \
  -fa on -fit off --no-mmap --no-repack --no-context-shift \
  -b 2048 -ub 512 -t 16 -tb 16 \
  --cache-type-k q8_0 --cache-type-v q8_0 \
  --spec-type draft-mtp \
  --spec-draft-device ROCm0 --spec-draft-ngl 999 \
  --spec-draft-n-max 2 --spec-draft-n-min 0 \
  --spec-draft-p-min 0.75 --spec-draft-p-split 0.10 \
  --cache-type-k-draft q8_0 --cache-type-v-draft q8_0 \
  --spec-draft-threads 16 --spec-draft-threads-batch 16 \
  --cache-ram 0 \
  --cache-disk "$CACHE_DIR" --cache-disk-limit 16384 \
  --temp 0.9 --top-p 1.0 --min-p 0.05 --top-k -1 \
  --repeat-penalty 1.0 --presence-penalty 0.0 \
  --jinja \
  --reasoning on --reasoning-format deepseek --reasoning-budget -1 \
  --metrics

Why these defaults:

n_max=2 and p_min=0.75 are the tested HY3 MTP operating point. Higher draft lengths can look deceptively good on tiny prompts and lose on longer or rejection-heavy traffic.
Target and draft KV stay at q8_0 for quality. The 64K context is the proven 128 GiB operating point; Hy3's larger native context does not mean every KV/runtime configuration will fit this hardware.
--cache-ram 0 avoids reserving an additional multi-GiB RAM cache. --cache-disk-limit 16384 permits up to 16 GiB of reusable prompt state on SSD.
--no-mmap is intentional on the tested UMA path, where the mmap loader stalled synchronously. --no-repack preserves the validated custom-kernel layout.
--no-context-shift is the safer production default for a fixed 64K agent slot. A forced context-shift stress run passed, but it is not needed for the recommended profile.

SSD prompt-cache behavior

The operational cache is not cosmetic. Exact-prefix time to first token fell to 0.264s from 7.328–9.316s cold; prefix-extension TTFP fell to 0.478s from 7.655–8.814s cold. Restored MTP acceptance remained 13/13 and 18/19, while measured SSD reads were 59.8ms and 88.3ms.

The cache test stored 21 states covering 79,675 tokens in about 14.05 GB on SSD while process RSS peaked near 2.00 GiB. The HA20 suite made 21 independent saves and no loads, so HA20 proves cache-on quality, memory behavior, and OOM safety; the dedicated prefix smoke tests prove cache-hit speed.

Files

File	Bytes	SHA-256
`Hy3-Chadrock-FPX-IFP2-MTP-00001-of-00005.gguf`	22,300,632,640	`e358ea9e5494960898e84b291f933bd7afbd657ae705a6ce872b3951c2f9b6c9`
`Hy3-Chadrock-FPX-IFP2-MTP-00002-of-00005.gguf`	22,707,214,816	`68eeeeaaef55f04b4534ac12c53703e0026075de9bd7e885f583bb8c3bc89fd2`
`Hy3-Chadrock-FPX-IFP2-MTP-00003-of-00005.gguf`	22,610,010,624	`fa506f07472b91e85de74b1c69eecbbc26348798286f30c56f65c647f85a4967`
`Hy3-Chadrock-FPX-IFP2-MTP-00004-of-00005.gguf`	22,177,392,768	`03c66ec1bc34e1589067da1110eefe2dae10db01e627a1bbb4b704f065ad5ba9`
`Hy3-Chadrock-FPX-IFP2-MTP-00005-of-00005.gguf`	7,658,646,848	`02e77fa1e3cd04e9fa82c48a73a834ab80db152149f5d148fe522d3bf0858c2f`

Load the first shard; the runner discovers the remaining four automatically. Do not merge the shards just to serve the model. Merging is unnecessary and temporarily needs roughly another 91 GiB of free disk space. If you already merged them with llama-gguf-split --merge, runner commit 647fd52 or newer can load the merged Hy3-Chadrock-FPX-IFP2-MTP.gguf; change only the -m path in the command above.

Provenance and limitations

Base model: tencent/Hy3, official BF16 revision 716aa724
Required runtime: ciru-ai/ROCmFPX main at 647fd52
Tested backend: ROCm on gfx1151 Strix Halo UMA
Tested context/profile: 64K, one slot, target and native-MTP draft KV at q8_0
This is a custom mixed quant and needs the linked runner; compatibility with CPU, CUDA, stock llama.cpp, or other third-party loaders is not claimed.
Tool and agent scores are useful evidence, not a guarantee. The 69% Safety & Boundaries result makes human oversight especially important for privileged tools.

Credits

Tencent Hunyuan for Hy3
llama.cpp and the ROCmFPX contributors for the runtime foundation
Ciru Inference Lab for the ROCmFP2 codebook, FPX-IFP2 recipe, importance-guided build, AMD runtime work, and evaluation

This repository distributes a quantized derivative under the base model's Apache-2.0 license. Review the original Hy3 model card before deployment.

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GGUF

Model size

299B params

Architecture

hy_v3

Hardware compatibility

We're not able to determine the quantization variants.

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tencent/Hy3

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