Instructions to use magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF",
	filename="Seed-OSS-36B-Instruct-unsloth-MXFP4_MOE-output_q6_K-router_gate_emb_q6_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF:Q6_K
# Run inference directly in the terminal:
llama-cli -hf magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF:Q6_K
# Run inference directly in the terminal:
llama-cli -hf magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF:Q6_K

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 magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF:Q6_K

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 magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF:Q6_K

Use Docker

docker model run hf.co/magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF:Q6_K

LM Studio
Jan

vLLM

How to use magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-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": "magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF:Q6_K

Ollama
How to use magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF with Ollama:
```
ollama run hf.co/magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF:Q6_K
```

Unsloth Studio new

How to use magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF 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 magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF 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 magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF to start chatting

Pi new

How to use magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF:Q6_K

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": "magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF:Q6_K

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 magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF:Q6_K

Run Hermes

hermes

Docker Model Runner
How to use magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF with Docker Model Runner:
```
docker model run hf.co/magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF:Q6_K
```

Lemonade

How to use magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull magiccodingman/Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF:Q6_K

Run and chat with the model

lemonade run user.Seed-OSS-36B-Instruct-Unsloth-MXFP4-Hybrid-GGUF-Q6_K

List all available models

lemonade list

magiccodingman commited on Nov 15, 2025

Commit

73dfc17

verified ·

1 Parent(s): 3c67abc

Update README.md

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@@ -1,3 +1,219 @@
----
-license: apache-2.0
----

+---
+license: apache-2.0
+tags:
+- mxfp4_hybrid
+- gguf
+- text-generation
+- quantized
+- cpu
+- gpu
+- mxfp4
+- mxfp4_moe
+- qwen3
+base_model:
+- unsloth/Seed-OSS-36B-Instruct
+---
+# Unsloth - Seed OSS 36B Instruct MXFP4 Hybrid GGUF
+**Dense model utilizing MXFP4_MOE with hybrid weights on a dense model. Achieving interesting results that show smaller file size, more TPS, and near lossless precision.**
+## **Use The Following Model!**
+Stats compared against the standard Q8_0 (precision loss still compared to F16)
+* **MXFP4_MOE-output_q6_K-router_gate_emb_q6_K**
+  8.8% smaller than Q8 • 21.82 TPS • 0.053% precision loss
+---
+This repository contains a set of hybrid MXFP4 quantized GGUF models designed to explore a surprising discovery:
+> A carefully targeted combination of MXFP4 + high-precision embeddings/output weights can deliver near-Q8 accuracy with Q4–Q6 level throughput and smaller file sizes than Q8.
+Unlike pure MXFP4, which heavily degrades dense models. This hybrid method selectively protects tensors that matter most for semantic stability, while allowing MXFP4 to accelerate everything else.
+> **This is experimental**. And should be treated as such. I am more than encouraging people to use this model and leave feedback! Though precision loss seemed near lossless, did the hybrid models act strange in certain situations? Worse or better on some topics compared to the original model? Did it do better/worse overall on everything? I'd love to hear back from others!
+---
+# The Magic Model
+This model achieved:
+> **File size reduction compared to the Q8_0**
+>
+> **Better precision loss scores than the pure Q6_K**
+>
+> **Achieving noticeably better TPS than a Q4_K_M**
+_I have personally deemed this in the category of "Q7.5" quantization._
+#### MXFP4_MOE-output_q6_K-router_gate_emb_q6_K
+> **(8.8% smaller than Q8 • 21.82 TPS • 0.053% precision loss )**
+This version created beat out everything in every way in the MXFP4 hybrid family created. Out of the batch, this MXFP4 hybrid was the only worth considering to utilize.
+The following was the conversion script:
+```bash
+llama-quantize \
+  --tensor-type token_embd.weight=Q6_K \
+  --tensor-type output.weight=Q6_K \
+  --tensor-type 'router.*'=Q6_K \
+  --tensor-type 'gate.*'=Q6_K \
+  "Path_To_F16_GGUF.gguf" \
+  "Path_To_GGUF.gguf" \
+  mxfp4_moe
+```
+---
+# MXFP4_MOE Hybrid Naming Scheme & Synopsis
+Multiple different combinations of converted models were created. The results were interesting to say the least. The following table will explain my naming scheme to what was done to the model to create it.
+| Suffix Example                      | Meaning                                |
+| ----------------------------------- | -------------------------------------- |
+| `MXFP4_MOE`                         | Pure MXFP4 pipeline                    |
+| `MXFP4_MOE-Q8`                      | Embedding/output in Q8_0               |
+| `MXFP4_MOE-F16`                     | Embedding/output in F16                |
+| `output_mxfp4-embd_q8`              | Output → MXFP4, Embedding → Q8         |
+| `output_mxfp4-router_gate_emb_q5_K` | Output → MXFP4, Emb/Router/Gate → Q5_K |
+| `MXFP4_MOE-Q6_K`                    | Both embedding + output in Q6_K        |
+| `Q8_0`, `Q6_K`, `Q4_K_M`            | Pure model-wide quantizations          |
+The results achieved were interesting to say the least. It was a brute force game of mass creating models with hybrid methods to find combinations that didn't cause too much noise and paired well with MXFP4.
+This repo showcases the converted models, whether good or bad that was created. But, I have been testing other models in different combinations as well. **The winning hybrid combinations shown in this repo DOES NOT always equate to the same results on different models.**
+Some models do better or worse with different kinds of combinations. It depends if it's dense, MOE, and much more. Many times the results surprise me. Many models no matter the combination will not play nice with MXFP4. At least with the methods shown here.
+---
+## Benchmark Methodology
+All models were tested with a unified automated harness using `llama.cpp` tools.
+**Included tests:**
+- **Throughput:**
+    `llama-bench` with descending GPU offload (`-ngl 35 → 0`) and automatic OOM retry.
+    Highest successful TPS is recorded.
+- **Perplexity:**
+    Three domains: **general**, **code**, **math**.
+    Each uses an auto-generated corpus of ~**32k tokens**.
+    Perplexity is computed with `llama-perplexity` at **2048-token** context.
+    Same GPU retry logic as above.
+- **Precision loss:**
+    Each model is compared to its **family F16 baseline**.
+    Precision-loss % is computed for all PPL domains, plus an averaged score.
+    Models are ranked by this metric.
+---
+### Table - Overview of Results
+Comparing to F16.
+| model_name                                  | size_reduction | tps_change |
+| ------------------------------------------- | -------------- | ---------- |
+| MXFP4_MOE-Q8                                | 46.87%         | 61.73%     |
+| Q8_0                                        | 46.87%         | 66.72%     |
+| MXFP4_MOE-F16                               | 40.46%         | 41.91%     |
+| MXFP4_MOE-output_q6_K-router_gate_emb_q6_K  | 51.57%         | 84.76%     |
+| MXFP4_MOE-Q6_K                              | 48.52%         | 66.55%     |
+| Q6_K                                        | 58.98%         | 97.63%     |
+| Q5_K_M                                      | 64.6%          | 90.69%     |
+| MXFP4_MOE-output_mxfp4-router_gate_emb_q6_K | 53.21%         | 82.98%     |
+| MXFP4_MOE-output_mxfp4-embd_q6_K            | 50.19%         | 74.01%     |
+| MXFP4_MOE-output_mxfp4-embd_q8              | 49.92%         | 64.94%     |
+| MXFP4_MOE-output_mxfp4-router_gate_emb_q8   | 49.92%         | 74.09%     |
+| MXFP4_MOE                                   | 73.42%         | 77.31%     |
+| Q4_K_M                                      | 69.9%          | 132.18%    |
+* All percentages compared against the selected family F16 baseline.
+---
+### Table - File Size + TPS + Avg Precision Loss
+| model_name                                  | file_size_gb | bench_tps | avg_prec_loss |
+| ------------------------------------------- | ------------ | --------- | ------------- |
+| F16                                         | 67.35        | 11.81     | 0             |
+| MXFP4_MOE-Q8                                | 35.78        | 19.1      | 0.0171        |
+| Q8_0                                        | 35.78        | 19.69     | 0.0171        |
+| MXFP4_MOE-F16                               | 40.1         | 16.76     | 0.0215        |
+| MXFP4_MOE-output_q6_K-router_gate_emb_q6_K  | 32.62        | 21.82     | 0.053         |
+| MXFP4_MOE-Q6_K                              | 34.67        | 19.67     | 0.0566        |
+| Q6_K                                        | 27.63        | 23.34     | 0.1651        |
+| Q5_K_M                                      | 23.84        | 22.52     | 0.2512        |
+| MXFP4_MOE-output_mxfp4-router_gate_emb_q6_K | 31.51        | 21.61     | 1.0377        |
+| MXFP4_MOE-output_mxfp4-embd_q6_K            | 33.55        | 20.55     | 1.0464        |
+| MXFP4_MOE-output_mxfp4-embd_q8              | 33.73        | 19.48     | 1.0473        |
+| MXFP4_MOE-output_mxfp4-router_gate_emb_q8   | 33.73        | 20.56     | 1.0473        |
+| MXFP4_MOE                                   | 17.9         | 20.94     | 2.694         |
+| Q4_K_M                                      | 20.27        | 27.42     | 2.8138        |
+* Bench NGL was 35
+* Utilized CUDA
+---
+### Table - PPL Columns
+| model_name | gen | gen_er | code | code_er | math | math_er |
+| ---------- | ---- | ------- | ----- | -------- | ------ | -------- |
+| F16 | 6.8905 | 0.1681 | 1.4129 | 0.0095 | 5.4475 | 0.121 |
+| MXFP4_MOE-Q8 | 6.8866 | 0.1679 | 1.413 | 0.0095 | 5.4474 | 0.121 |
+| Q8_0 | 6.8866 | 0.1679 | 1.413 | 0.0095 | 5.4474 | 0.121 |
+| MXFP4_MOE-F16 | 6.8893 | 0.1679 | 1.4132 | 0.0095 | 5.4508 | 0.1211 |
+| MXFP4_MOE-output_q6_K-router_gate_emb_q6_K | 6.8932 | 0.1682 | 1.4127 | 0.0095 | 5.4548 | 0.1213 |
+| MXFP4_MOE-Q6_K | 6.8946 | 0.1682 | 1.4128 | 0.0095 | 5.4539 | 0.1213 |
+| Q6_K | 6.9012 | 0.1685 | 1.4135 | 0.0095 | 5.4637 | 0.1218 |
+| Q5_K_M | 6.9071 | 0.1685 | 1.4168 | 0.0096 | 5.4604 | 0.1212 |
+| MXFP4_MOE-output_mxfp4-router_gate_emb_q6_K | 6.9647 | 0.169 | 1.4196 | 0.0095 | 5.5326 | 0.1227 |
+| MXFP4_MOE-output_mxfp4-embd_q6_K | 6.9649 | 0.1691 | 1.4199 | 0.0095 | 5.5327 | 0.1226 |
+| MXFP4_MOE-output_mxfp4-embd_q8 | 6.9638 | 0.1691 | 1.4198 | 0.0095 | 5.5341 | 0.1227 |
+| MXFP4_MOE-output_mxfp4-router_gate_emb_q8 | 6.9638 | 0.1691 | 1.4198 | 0.0095 | 5.5341 | 0.1227 |
+| MXFP4_MOE | 7.1007 | 0.1728 | 1.4351 | 0.0097 | 5.636 | 0.1239 |
+| Q4_K_M | 7.0964 | 0.1759 | 1.4235 | 0.0098 | 5.7037 | 0.1303 |
+* gen = ppl_general
+* gen_er = ppl_general_error
+* code = ppl_code
+* code_er = ppl_code_error
+* math = ppl_math
+* math_er = ppl_math_error
+---
+### Table - Precision Loss Columns
+| model_name | loss_general | loss_code | loss_math |
+| ---------- | ------------ | ---------- | ---------- |
+| F16 | 0 | 0 | 0 |
+| MXFP4_MOE-Q8 | -0.0566 | 0.0071 | -0.0018 |
+| Q8_0 | -0.0566 | 0.0071 | -0.0018 |
+| MXFP4_MOE-F16 | -0.0174 | 0.0212 | 0.0606 |
+| MXFP4_MOE-output_q6_K-router_gate_emb_q6_K | 0.0392 | -0.0142 | 0.134 |
+| MXFP4_MOE-Q6_K | 0.0595 | -0.0071 | 0.1175 |
+| Q6_K | 0.1553 | 0.0425 | 0.2974 |
+| Q5_K_M | 0.2409 | 0.276 | 0.2368 |
+| MXFP4_MOE-output_mxfp4-router_gate_emb_q6_K | 1.0768 | 0.4742 | 1.5622 |
+| MXFP4_MOE-output_mxfp4-embd_q6_K | 1.0797 | 0.4954 | 1.564 |
+| MXFP4_MOE-output_mxfp4-embd_q8 | 1.0638 | 0.4884 | 1.5897 |
+| MXFP4_MOE-output_mxfp4-router_gate_emb_q8 | 1.0638 | 0.4884 | 1.5897 |
+| MXFP4_MOE | 3.0506 | 1.5712 | 3.4603 |
+| Q4_K_M | 2.9882 | 0.7502 | 4.7031 |
+* loss_general = precision_loss_general_pct
+* loss_code = precision_loss_code_pct
+* loss_math = precision_loss_math_pct