Instructions to use patrickbdevaney/MiMo-V2.6-Flash-REAP50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use patrickbdevaney/MiMo-V2.6-Flash-REAP50 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="patrickbdevaney/MiMo-V2.6-Flash-REAP50", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("patrickbdevaney/MiMo-V2.6-Flash-REAP50", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use patrickbdevaney/MiMo-V2.6-Flash-REAP50 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "patrickbdevaney/MiMo-V2.6-Flash-REAP50"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "patrickbdevaney/MiMo-V2.6-Flash-REAP50",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/patrickbdevaney/MiMo-V2.6-Flash-REAP50

SGLang

How to use patrickbdevaney/MiMo-V2.6-Flash-REAP50 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "patrickbdevaney/MiMo-V2.6-Flash-REAP50" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "patrickbdevaney/MiMo-V2.6-Flash-REAP50",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "patrickbdevaney/MiMo-V2.6-Flash-REAP50" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "patrickbdevaney/MiMo-V2.6-Flash-REAP50",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use patrickbdevaney/MiMo-V2.6-Flash-REAP50 with Docker Model Runner:
```
docker model run hf.co/patrickbdevaney/MiMo-V2.6-Flash-REAP50
```

patrickbdevaney commited on 8 days ago

Commit

eb82874

verified ·

1 Parent(s): abdcba9

Remove stale README_upstream.md draft; card updated (2026-11-06)

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README.md +0 -1
README_upstream.md +0 -75

README.md CHANGED Viewed

@@ -140,7 +140,6 @@ Code, gates and the method write-ups are in the pipeline repository: [patrickbde
 - **Long context is unverified.** Calibration never saw more than 4,095 tokens, and the 1M figure is config plus memory arithmetic.
 - **Multimodal inputs are untested.** The towers are unchanged, and the calibration fed real image, audio and video through them. No multimodal output from the pruned model has been checked.
 - **Draft heads.** The base repo's separate `dflash/` speculative-decoding drafter is **not** included: it was trained against the unpruned model, and nobody has checked it against this one. The 3 native MTP layers are included unchanged; their acceptance rate on the pruned trunk has not been measured.
-- `README_upstream.md` in this repo is an early draft of this card, not Xiaomi's model card. Some of its wording (for example "fits and serves") was never verified. Use this README instead.
 - Calibration covered the nine domains above. Other domains and languages were not measured.
 ## Changelog / corrections

 - **Long context is unverified.** Calibration never saw more than 4,095 tokens, and the 1M figure is config plus memory arithmetic.
 - **Multimodal inputs are untested.** The towers are unchanged, and the calibration fed real image, audio and video through them. No multimodal output from the pruned model has been checked.
 - **Draft heads.** The base repo's separate `dflash/` speculative-decoding drafter is **not** included: it was trained against the unpruned model, and nobody has checked it against this one. The 3 native MTP layers are included unchanged; their acceptance rate on the pruned trunk has not been measured.
 - Calibration covered the nine domains above. Other domains and languages were not measured.
 ## Changelog / corrections

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@@ -1,75 +0,0 @@
----
-license: mit
-base_model: XiaomiMiMo/MiMo-V2.6-Flash-RL
-library_name: transformers
-tags:
-- moe
-- pruned
-- reap
-- hope
-- mimo_v2
-- multimodal
----
-# MiMo-V2.6-Flash-REAP50
-`XiaomiMiMo/MiMo-V2.6-Flash-RL` with **50% of its routed experts removed** — 256
-experts per layer down to **128** — so that it fits and serves on a single
-NVIDIA Jetson AGX Thor (117 GiB unified memory). 86.1 GiB across 65 shards.
-Vision, audio and video input are preserved; `audio_tokenizer/` ships with the checkpoint.
-## How the experts were chosen
-Not by activation frequency. Expert saliency was accumulated over a calibration corpus and the
-prune set was solved with **HOPE**, which minimises the output error a prune set actually causes
-including the *interaction* terms between experts — REAP is the same objective with the
-off-diagonal zeroed, and that off-diagonal cannot be recovered after the pass.
-| setting | value |
-|---|---|
-| objective | `hope` |
-| saliency criterion | `reap_1_1_1` |
-| prune ratio | 0.50, uniform across layers |
-| per-domain protection | top 8% of every domain held out of the prune set |
-| worst domain retained | 0.9935 (audio) |
-| mean retained | 0.9964 |
-| HOPE objective pᵀFp | 0.01624 |
-Selection is scored per domain and ranked by the **worst** one, never the mean: an average is
-how a criterion that destroys one capability outscores one that preserves all of them.
-### Retained gated output mass, by calibration domain
-| domain | retention |
-|---|---|
-| audio | 0.9935 |
-| image | 0.9937 |
-| video | 0.9938 |
-| science | 0.9973 |
-| math | 0.9974 |
-| finance | 0.9974 |
-| ballast | 0.9976 |
-| code | 0.9983 |
-| agentic | 0.9983 |
-## Routers
-Pruning an expert leaves its router column behind. The routers were refitted by output matching
-against the unpruned teacher, routers only, every expert frozen: 47 routers refitted; all 47 kept at the teacher weights (the fit did not beat the baseline).
-A refit that failed to beat the untouched baseline was discarded in favour of the baseline, so
-no router here is worse than simply slicing the teacher's.
-## Limitations
-- Calibration was English/Chinese text, code, math, science, finance, agentic traces, and
-  image/audio/video captions. Domains outside that mix were not measured.
-- The `dflash/` speculative-decoding draft head from the source repo is **not** included: it was
-  trained against the unpruned expert set and is not valid for this checkpoint.
-- Pruned MoE experts do not come back. This is a lossy, irreversible transform of the base model.
-## Provenance
-Produced by [patrickbdevaney/xiaomi-2.6-flash-REAP](https://github.com/patrickbdevaney/xiaomi-2.6-flash-REAP)
-on a single Jetson AGX Thor. MIT, inherited from the base model — attribution to Xiaomi MiMo.