Instructions to use Myric/KAT-Coder-V2.5-Dev-MTP-head with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Myric/KAT-Coder-V2.5-Dev-MTP-head 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 Myric/KAT-Coder-V2.5-Dev-MTP-head
# Run inference directly in the terminal:
llama cli -hf Myric/KAT-Coder-V2.5-Dev-MTP-head

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Myric/KAT-Coder-V2.5-Dev-MTP-head
# Run inference directly in the terminal:
llama cli -hf Myric/KAT-Coder-V2.5-Dev-MTP-head

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 Myric/KAT-Coder-V2.5-Dev-MTP-head
# Run inference directly in the terminal:
./llama-cli -hf Myric/KAT-Coder-V2.5-Dev-MTP-head

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 Myric/KAT-Coder-V2.5-Dev-MTP-head
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Myric/KAT-Coder-V2.5-Dev-MTP-head

Use Docker

docker model run hf.co/Myric/KAT-Coder-V2.5-Dev-MTP-head

LM Studio
Jan
Ollama
How to use Myric/KAT-Coder-V2.5-Dev-MTP-head with Ollama:
```
ollama run hf.co/Myric/KAT-Coder-V2.5-Dev-MTP-head
```

Unsloth Studio

How to use Myric/KAT-Coder-V2.5-Dev-MTP-head 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 Myric/KAT-Coder-V2.5-Dev-MTP-head 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 Myric/KAT-Coder-V2.5-Dev-MTP-head to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Myric/KAT-Coder-V2.5-Dev-MTP-head to start chatting

How to use Myric/KAT-Coder-V2.5-Dev-MTP-head with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Myric/KAT-Coder-V2.5-Dev-MTP-head

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": "Myric/KAT-Coder-V2.5-Dev-MTP-head"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Myric/KAT-Coder-V2.5-Dev-MTP-head with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Myric/KAT-Coder-V2.5-Dev-MTP-head

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 "Myric/KAT-Coder-V2.5-Dev-MTP-head" \
  --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 Myric/KAT-Coder-V2.5-Dev-MTP-head with Docker Model Runner:
```
docker model run hf.co/Myric/KAT-Coder-V2.5-Dev-MTP-head
```

Lemonade

How to use Myric/KAT-Coder-V2.5-Dev-MTP-head with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Myric/KAT-Coder-V2.5-Dev-MTP-head

Run and chat with the model

lemonade run user.KAT-Coder-V2.5-Dev-MTP-head-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Myric/KAT-Coder-V2.5-Dev-MTP-head with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Myric/KAT-Coder-V2.5-Dev-MTP-head

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 Myric/KAT-Coder-V2.5-Dev-MTP-head

Run Hermes

hermes

Atomic Chat

Myric commited on 3 days ago

Commit

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1 Parent(s): eb6896c

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@@ -56,6 +56,38 @@ un-fine-tuned transplant is already a real ~20-40% speedup, not a wash — accep
 by task in a way that tracks predictability (the BST task, mostly standard tree-traversal
 boilerplate, hits 89%; the LRU cache, with genuine locking/eviction logic, is the hardest at 61%).
 ## Attribution
 - KAT-Coder base model: **Kwaipilot** — [Kwaipilot/KAT-Coder-V2.5-Dev](https://huggingface.co/Kwaipilot/KAT-Coder-V2.5-Dev) (Apache-2.0).

 by task in a way that tracks predictability (the BST task, mostly standard tree-traversal
 boilerplate, hits 89%; the LRU cache, with genuine locking/eviction logic, is the hardest at 61%).
+## Full coding-benchmark validation (real agentic tasks, not just completion speed)
+Ran the same two task suites used elsewhere in this project's Qwen3.6-lineage work (an existing
+5-task hard tier, plus a new 3-task tier designed to require genuine algorithmic reasoning rather
+than spec-transcription) against this MTP-enabled build vs the same quant without MTP, via
+`opencode`'s real agentic tool-calling loop (not just raw completion). **Correctness was
+identical — 100% on both suites, both with and without MTP** — exactly what the theory predicts:
+the MTP head only drafts candidate tokens, the main model always verifies against its own true
+computation, so a bad draft just gets rejected and never reaches the output. Speed differed
+substantially:
+| hard-tier task | no MTP | with MTP | speedup |
+|---|---:|---:|---:|
+| batch_scheduler | 22.5s | 20.8s | 1.08x |
+| buddy_allocator | 123.5s | 63.5s | 1.94x |
+| circuit_breaker | 50.7s | 14.0s | 3.62x |
+| expr_parser_advanced | 54.7s | 21.1s | 2.59x |
+| inventory_fix_and_extend | 16.4s | 12.3s | 1.33x |
+| **total** | **267.8s** | **131.7s** | **2.03x** |
+| tier-4 task | no MTP | with MTP | speedup |
+|---|---:|---:|---:|
+| lru_ttl_cache | 20.5s | 25.2s | 0.81x |
+| rate_limiter_fix_and_extend | 19.7s | 12.1s | 1.63x |
+| weighted_interval_scheduling | 22.5s | 17.4s | 1.29x |
+| **total** | **62.7s** | **54.7s** | **1.15x** |
+Both suites hit 100% (59/59 and 28/28 test cases respectively) in both configurations. The one
+slower result (`lru_ttl_cache`, 0.81x) is most likely ordinary run-to-run variance in how much
+iteration that specific attempt needed, not a systematic MTP cost — every other task improved, and
+the hard-tier set as a whole is a genuine 2x.
 ## Attribution
 - KAT-Coder base model: **Kwaipilot** — [Kwaipilot/KAT-Coder-V2.5-Dev](https://huggingface.co/Kwaipilot/KAT-Coder-V2.5-Dev) (Apache-2.0).