Instructions to use jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF", device_map="auto")

llama-cpp-python

How to use jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF",
	filename="Ornith-1.0-35B-A3B-MXFP4_MOE_Q8_0-Imatrix.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 jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF 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 jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q
# Run inference directly in the terminal:
llama cli -hf jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q
# Run inference directly in the terminal:
llama cli -hf jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q

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 jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q
# Run inference directly in the terminal:
./llama-cli -hf jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q

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 jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q

Use Docker

docker model run hf.co/jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q

LM Studio
Jan

vLLM

How to use jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-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": "jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q

SGLang

How to use jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF 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 "jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF" \
    --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": "jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF" \
        --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": "jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF with Ollama:
```
ollama run hf.co/jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q
```

Unsloth Studio

How to use jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-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 jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-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 jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF to start chatting

How to use jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q

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": "jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q

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 jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q

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 "jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q" \
  --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 jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q
```

Lemonade

How to use jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF:MXFP4_MOE_Q

Run and chat with the model

lemonade run user.Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF-MXFP4_MOE_Q

List all available models

lemonade list

💎 Ornith-1.0-35B-A3B - Custom Mixed Precision GGUFs with Imatrix

Ornith-1.0, a self-improving family of open-source models specially for agentic coding tasks.
Built on top of pretrained Gemma 4 and Qwen 3.5, it achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks.

This repository contains custom, highly optimized, multi-tier mixed precision GGUF weights for deepreinforce-ai/Ornith-1.0-35B.

Ornith-1.0 35B achieves state-of-the-art performance among open-source models of comparable size across a broad range of agentic coding benchmarks.

Highly Recommended: Always keep reasoning/thinking enabled.
Ornith thoroughly plans and reasons through code edits before execution, ensuring an efficient and clean output.
Unlike baseline Qwen models, which frequently execute blindly and backtrack after generating broken code.

To learn more about Ornith 1.0, read their blog post.
To learn more about how to use Ornith 1.0 35B, view the base model.
A smaller variant is also available: Ornith-1.0-9B

These quants were generated using manual layer targeting to maximize quality while shrinking the massive VRAM footprint of the Mixture of Experts layers.

📄 GGUF Files

In order of quality:

Filename	Size	Quants
Ornith-1.0-35B-A3B-MXFP4_MOE_Q8_0_F16-Imatrix.gguf	20.7 GB	MXFP4_MOE + Q8_0 + F16
Ornith-1.0-35B-A3B-MXFP4_MOE_Q8_0-Imatrix.gguf	19.8 GB	MXFP4_MOE + Q8_0
Ornith-1.0-35B-MXFP4_MOE-Only-Imatrix.gguf	18.5 GB	MXFP4_MOE Only

Updated 2026-07-08:

Added Ornith-1.0-35B-MXFP4_MOE-Only-Imatrix.gguf

Updated 2026-07-04:

Created a bigger imatrix and requantized the models

📊 Importance Matrix (Imatrix)

Expand to view datasets & details

The following datasets were used for the imatrix:

I am currently remaking this imatrix with 2048 ctx, 800 chunks, with smaller sets of text, and:
- Ornith-generated agent skills markdown text & project documentation markdown text
- Ornith-generated sub-agent prompt+result markdown text
Old/Current: Custom Target Matrix (2048 ctx with 600 chunks), up to a max of 100MB of each:
- eaddario/imatrix-calibration - tools_huge
- Glint-Research/Fable-5-traces
- lordx64/fable-sft-combined-v2
- angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k
- nohurry/Opus-4.6-Reasoning-3000x-filtered
- eaddario/imatrix-calibration - code_huge, math_huge
- osunlp/QUEST-RL-Data
- osunlp/QUEST-SFT-Data-Open-ended
- m-a-p/CodeFeedback-Filtered-Instruction
- TuringEnterprises/Rubric-Graded-Reasoning - Rubric-CS, Rubric-DS

🔍 Precision Matrix & Flavor Variations

Standard global quantization presets (like stock MXFP4_MOE) compress the backbone layers uniformly, which degrades the delicate reasoning capabilities of advanced agent models.
This repository provides multiple distinct manual configuration layouts to balance precision and memory constraints:

1. The Tri-Quant Hybrid Flavor (`MXFP4` + `Q8_0` + `F16`)

Ornith-1.0-35B-A3B-MXFP4_MOE_Q8_0_F16-Imatrix.gguf - Designed for maximum quality preservation, this layout implements a strict 3-Tier Precision Matrix:

Tier 1 (Core & Mamba Gating - F16 Precision):
- token_embd.weight, output.weight - Protects the critical input/output vocabulary mappings. Dramatically prevents text degradation.
- ssm_alpha, ssm_beta - Protects the integrity of the Mamba state-space calculations across long-range context tokens.
Tier 2 (Backbone & Shared - Q8_0 Precision): ssm_out, *._shexp - Keeps the attention mechanics, and all trailing shared experts at high quality, to protect the logical research loops.
Tier 3 (Routed Experts - MXFP4 Precision): ffn_down_exps, ffn_gate_exps, ffn_up_exps - Shrink the massive background expert parameters directly to MXFP4.

2. The Dual-Quant Hybrid Flavor (`MXFP4` + `Q8_0`)

Ornith-1.0-35B-A3B-MXFP4_MOE_Q8_0-Imatrix.gguf - Designed for a slightly leaner memory profile, this layout utilizes 2-Tier Precision:

Tier 1 (Backbone - Q8_0 Precision): All attention blocks, Mamba structures, vocabulary embeddings, and internal routers use the universal Q8_0 format.
Tier 2 (Experts - MXFP4 Precision): The heavy sparse expert blocks are target-quantized directly to MXFP4.

3. Bonus Single-Quant (MXFP4)

Ornith-1.0-35B-MXFP4_MOE-Only-Imatrix.gguf - Using only MXFP4, this shrinks the model down to 18.5 GB. The quality is not the best, but it can still do decent work.

Single Tier (All Layers - MXFP4 Precision): All layers are target-quantized directly to MXFP4, for speed and a low VRAM footprint.

📝 Exact Conversion Details

These files were converted via llama-quantize utilizing the following manual recipe parameters:

Convert SafeTensors to GGUF:

# Requires python3.12, with `pip install --upgrade transformers`
python convert_hf_to_gguf.py "Ornith-1.0-35B/" --outtype f16 --outfile "Ornith-1.0-35B_F16.gguf"

Generate Tri-Quant MXFP4_MOE + Q8_0 + F16:

llama-quantize \
  --tensor-type ".*_shexp\.weight=Q8_0" \
  --tensor-type "token_embd\.weight=F16" \
  --tensor-type "^output\.weight=F16" \
  --tensor-type "blk\..*\.(ssm_alpha|ssm_beta)\.weight=F16" \
  --tensor-type "blk\..*\.(ffn_down_exps|ffn_gate_exps|ffn_up_exps)\.weight=MXFP4" \
  --imatrix "imatrix.gguf" \
  "Ornith-1.0-35B_F16.gguf" \
  "Ornith-1.0-35B-A3B-MXFP4_MOE_Q8_0_F16-Imatrix.gguf" \
  Q8_0

Generate Dual-Quant MXFP4_MOE + Q8_0:

llama-quantize \
  --tensor-type ".*_shexp\.weight=Q8_0" \
  --tensor-type "blk\..*\.(ffn_down_exps|ffn_gate_exps|ffn_up_exps)\.weight=MXFP4" \
  --imatrix "imatrix.gguf" \
  "Ornith-1.0-35B_F16.gguf" \
  "Ornith-1.0-35B-A3B-MXFP4_MOE_Q8_0-Imatrix.gguf" \
  Q8_0

Generate Single-Quant MXFP4_MOE:

llama-quantize \
  --tensor-type ".*_shexp\.weight=MXFP4" \
  --tensor-type "token_embd\.weight=MXFP4" \
  --tensor-type "^output\.weight=MXFP4" \
  --tensor-type "blk\..*\.(ssm_alpha|ssm_beta|ssm_out|attn_gate|attn_qkv|ffn_down|ffn_gate|ffn_up|attn_k|attn_q|attn_v|attn_output)\.weight=MXFP4" \
  --tensor-type "blk\..*\.(ffn_down_exps|ffn_gate_exps|ffn_up_exps)\.weight=MXFP4" \
  --imatrix "imatrix.gguf" \
  "Ornith-1.0-35B_F16.gguf" \
  "Ornith-1.0-35B-MXFP4_MOE-Only-Imatrix.gguf" \
  MXFP4_MOE

📝 Local Deployment & `llama-server` Configuration (`config.ini`)

To maintain the rock-solid reasoning loop and prevent agents from falling into repetitive tool-calling deadlocks, use the following server parameter recommendations (Similar to other Qwen 3.5+ configurations).

# --- Samplers (Dynamic & Expressive) ---
temperature = 0.55
top-k = 15
top-p = 0.90
min-p = 0.12
# --- Penalties (Prevent Syntax & Reasoner Corruption) ---
repeat-penalty = 1.08
presence-penalty = 0.00
# --- DRY Sampler (Protects Indentation & Structural Boilerplate) ---
dry-multiplier = 0.8
dry-base = 1.75
dry-allowed-length = 3
dry-penalty-last-n = 1024
dry-sequence-breaker = ["\n", "```\n", ":", "\t", "\"", "|", "-", "}", "]"]
# --- Enforced Execution Graph ---
samplers = top_k;top_p;min_p;temp;dry

Highly Recommended: Always keep reasoning/thinking enabled, for better quality results.

# --- Reasoning ---
chat-template-kwargs = { "enable_thinking":true }
reasoning = on
reasoning-budget = 20480
reasoning-format = auto

This works well with 256k context window.

For Maximum Quality at 100k+ Context:
Use the MXFP4_MOE + Q8_0 + F16 split-quantized version.

Preserved at F16: token_embd.weight, output.weight, *.ssm_alpha.weight, and *.ssm_beta.weight.

Why this matters: Keeping these critical layers at full precision prevents the model from dropping fine details during extreme "needle-in-a-haystack" retrieval tasks (large context windows).

What to avoid: If output.weight or the embedding layers are quantized to Q8_0 or lower, logit precision rounds off, causing the model to lose accuracy and forget specific details in long-context scenarios.

Updated 2026-07-17:

Improve sampling settings again (no more looping for me)

Updated 2026-07-08:

Improve sampling settings to lessen reasoning loops (also faster token-generation due to temp & min-p changes)

ℹ️ Misc Details

I'm doing this as a side hobby, with my AMD 5900X, 64GB DDR4, RTX 3060 12GB & RTX 5060 Ti 16GB.

In addition to the above configuration, I also use:

slots = 1
parallel = 1
no-warmup = true

flash-attn = on
mlock = false
no-mmap = false
context-shift = false

batch-size = 2048
ubatch-size = 256

fit = on
fit-target = 768
main-gpu = 0
split-mode = layer
n-gpu-layers = 999
n-cpu-moe = 0
tensor-split = 16,12
override-tensor = (token_embd)=CUDA0,(vision|vpm|nextn)=CPU

fit-ctx = 262144
cache-type-k = q8_0
cache-type-v = q8_0

jinja = true
chat-template = jinja
chat-template-file = chat_template.jinja

For further quality and better ssm behaviour, this configuration can help:

context-shift = false
cache-type-k = f16
cache-type-v = f16

🤝 Support the Journey

As a passionate developer, I'm always programming, automating, or experimenting with new ideas.
I love building open-source tools, trying out new web tech, and creating things that don't yet exist, including local AI & quantizing models.

I love sharing these creations to give back to the community.
If my projects have saved you time or helped you out, consider supporting my work below!

👉 Support me on Ko-fi

✨ Acknowledgments

deepreinforce-ai for the exceptional Ornith-1.0 base model.

📜 License

Released under MIT.

🔗 Citation

@misc{ornith-35b,
    title = {{Ornith-1.0-35B}: Agentic Coding, Open to All},
    url = {https://deep-reinforce.com/ornith_1_0.html},
    author = {{DeepReinforce Team}},
    year = {2026}
}

Downloads last month: 18,811

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

16-bit

View +1 variant

Model tree for jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF

Base model

deepreinforce-ai/Ornith-1.0-35B

Quantized

(155)

this model

Collections including jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF

Agentic/Programming Models

Collection

5 items • Updated 13 days ago

Research/Analysis Models

Collection

3 items • Updated 17 days ago

💎 Ornith-1.0-35B-A3B - Custom Mixed Precision GGUFs with Imatrix

📄 GGUF Files

📊 Importance Matrix (Imatrix)

🔍 Precision Matrix & Flavor Variations

1. The Tri-Quant Hybrid Flavor (MXFP4 + Q8_0 + F16)

2. The Dual-Quant Hybrid Flavor (MXFP4 + Q8_0)

3. Bonus Single-Quant (MXFP4)

📝 Exact Conversion Details

Convert SafeTensors to GGUF:

Generate Tri-Quant MXFP4_MOE + Q8_0 + F16:

Generate Dual-Quant MXFP4_MOE + Q8_0:

Generate Single-Quant MXFP4_MOE:

📝 Local Deployment & llama-server Configuration (config.ini)

ℹ️ Misc Details

🤝 Support the Journey

✨ Acknowledgments

📜 License

🔗 Citation

Model tree for jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF

Collections including jashepp/Ornith-1.0-35B-A3B-MXFP4_MOE_Hybrid-Imatrix-GGUF

1. The Tri-Quant Hybrid Flavor (`MXFP4` + `Q8_0` + `F16`)

2. The Dual-Quant Hybrid Flavor (`MXFP4` + `Q8_0`)

📝 Local Deployment & `llama-server` Configuration (`config.ini`)