Text Generation
MLX
Safetensors
hy_v3
mtplx
Mixture of Experts
quantized
imatrix
2-bit
streaming
hunyuan
conversational
Instructions to use OpensourceWTF/Hy3-oQ2e-MTPLX-streaming with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OpensourceWTF/Hy3-oQ2e-MTPLX-streaming with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("OpensourceWTF/Hy3-oQ2e-MTPLX-streaming") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use OpensourceWTF/Hy3-oQ2e-MTPLX-streaming with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "OpensourceWTF/Hy3-oQ2e-MTPLX-streaming"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "OpensourceWTF/Hy3-oQ2e-MTPLX-streaming" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use OpensourceWTF/Hy3-oQ2e-MTPLX-streaming with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "OpensourceWTF/Hy3-oQ2e-MTPLX-streaming"
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 OpensourceWTF/Hy3-oQ2e-MTPLX-streaming
Run Hermes
hermes
- OpenClaw new
How to use OpensourceWTF/Hy3-oQ2e-MTPLX-streaming with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "OpensourceWTF/Hy3-oQ2e-MTPLX-streaming"
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 "OpensourceWTF/Hy3-oQ2e-MTPLX-streaming" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- MLX LM
How to use OpensourceWTF/Hy3-oQ2e-MTPLX-streaming with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "OpensourceWTF/Hy3-oQ2e-MTPLX-streaming"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "OpensourceWTF/Hy3-oQ2e-MTPLX-streaming" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpensourceWTF/Hy3-oQ2e-MTPLX-streaming", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 4,886 Bytes
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license: apache-2.0
license_link: https://huggingface.co/tencent/Hy3/blob/main/LICENSE
base_model: tencent/Hy3
base_model_relation: quantized
library_name: mlx
pipeline_tag: text-generation
tags:
- mlx
- mtplx
- moe
- quantized
- imatrix
- 2-bit
- streaming
- hunyuan
---
# Hy3 oQ2e MTPLX-streaming
2-bit Hunyuan 3.0 (295B-A21B MoE) for Apple Silicon, packaged for
SSD-streamed serving with [MTPLX](https://github.com/davidtai/MTPLX).
Weights stream from SSD under a configurable memory envelope, so the model
runs on Macs that cannot hold it resident. Measured envelopes: 64, 80, and
88 GiB of weight budget on a 128 GB M5 Max.
Quantization: omlx oQe level-2 imatrix experts (2.44 bpw, 2.50 bpw
effective with q8 residents), from
[mlx-community/Hy3-oQ2e](https://huggingface.co/mlx-community/Hy3-oQ2e).
## Repo contents (~91 GiB)
| files | size | what |
|---|---|---|
| `model-resident-*.safetensors` (2) + `model.safetensors.index.json` | 8.7 GiB | attention, routers, shared experts, embeddings, dense layer 0 (q8-gs64); the index covers exactly these resident tensors |
| `experts.bin` + `expert-manifest.json` | 75 GiB | all 15,168 routed expert records (2-bit gs128) as an aligned, hash-pinned bank; the manifest is sidecar-authoritative (references resident shards + the bank only) |
| `expert-manifest.shards.json` | — | provenance manifest pinning sha256 for the pre-repack safetensors shards and every expert record |
| `mtp/layer80-bf16.safetensors`, `mtp/layer80-residents-q.safetensors` | 7.1 GiB | Hy3 MTP head (speculative decoding) |
| `island-placement.json`, `route-census.json` | — | per-machine seeds; MTPLX regenerates them on first serve |
This is the MTPLX streaming layout: routed experts ship only in
`experts.bin`, so nothing is downloaded twice. Download everything:
```bash
hf download OpensourceWTF/Hy3-oQ2e-MTPLX-streaming
```
**Plain-MLX resident serving (128 GB Mac):** use the upstream safetensors
checkpoint at
[mlx-community/Hy3-oQ2e](https://huggingface.co/mlx-community/Hy3-oQ2e);
this repo packages the same bytes for streaming.
**llama.cpp:** not served. MLX affine quantization does not convert to
GGML blocks in either direction.
## Measured decode (M5 Max, 614 GB/s)
Real long-code prefill (exact-token gated), greedy, decode 256, bf16 KV,
3 reps. Cell = decode tok/s, mode of record = faster of AR / K1 (MTP
speculative depth 1).
| weight envelope | 1024 ctx | 16k | 64k |
|---|---|---|---|
| 88 GiB (full residency) | **48.0** K1 | **27.6** K1 | — (KV exceeds the 112 GiB budget) |
| 80 GiB | **21.6** K1 | **15.9** AR | **9.2** AR |
| 64 GiB | **11.1** K1 | **7.0** AR | **4.0** AR |
Short-prompt arm (320-token code prompt): K1 47.7 / 22.3 / 10.8 on
88/80/64 — K1 wins every envelope there.
Two regimes govern the mode choice. K1 wins at short context on every
envelope, and at every context under full residency. AR wins at 16k+ on
streaming envelopes: speculative verify multiplies expert-miss servicing
under partial residency. Acceptance is 0.89–0.90 on code in every cell,
so the crossover is a decode-cost effect, not an acceptance effect.
## Quality (serving config: rq4 projections, bf16 KV)
| eval | score |
|---|---|
| MBPP full-974 | 0.8004 (identical to the q8-resident reference) |
| HumanEval-164 | 0.8659 (q8 reference 0.8720, McNemar exact p = 1.0) |
## Serving notes
- Presets `hy3-oq2e-rq4-{48..96}` ship in the MTPLX repo (main branch).
- **bf16 KV is required for MTP.** kv4 was measured and reversed:
acceptance collapses 0.898 → 0.125 and every MTP lane loses to AR.
- Recommended: K1 for short context, AR for 16k+ on streaming envelopes.
- Residents are pre-quantized q8-gs64 and load via the config-driven
path; never requantize them at load.
## Provenance and integrity
- Base model [tencent/Hy3](https://huggingface.co/tencent/Hy3)
(Apache-2.0); quantized bank mlx-community/Hy3-oQ2e rev `1979c306`.
- Re-sharded here into resident/expert groups for selective download.
Tensor bytes are unchanged; every tensor was sha256-verified at repack.
- `expert-manifest.json` (sidecar-authoritative, what the engine loads)
pins the resident shards and `experts.bin`; it is what makes the
expert-shard-free selective download servable.
- `expert-manifest.shards.json` pins sha256 for all 18 safetensors
shards, all 15,168 expert records, and `experts.bin`.
- Two metadata corrections vs the published checkpoint (originals kept as
`*.orig-published`): index `total_size` (overstated upstream by
345,252 B) and config `num_nextn_predict_layers` 0→1, which the bundled
MTP head requires.
## Not measured
32k context. 48/32 GiB envelopes (presets exist, never benchmarked).
kv4 beyond the single 88 GiB × 16k cell. Prose-heavy workloads: MTP
acceptance is content-dependent (0.52 measured on prose vs 0.89–0.90 on
code); the speed table is a code-workload table.
|