Instructions to use Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated 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 Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated
# Run inference directly in the terminal:
llama cli -hf Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated
# Run inference directly in the terminal:
llama cli -hf Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated

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 Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated
# Run inference directly in the terminal:
./llama-cli -hf Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated

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 Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated

Use Docker

docker model run hf.co/Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated

LM Studio
Jan

vLLM

How to use Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated

Ollama
How to use Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated with Ollama:
```
ollama run hf.co/Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated
```
Unsloth Desktop

How to use Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated with Docker Model Runner:
```
docker model run hf.co/Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated
```

Lemonade

How to use Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated

Run and chat with the model

lemonade run user.Qwen3.8-Distill-35B-A3B-Coder-Abliterated-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated

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 Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated

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 "Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated" \
  --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"

Qwen3.8-Distill-35B-A3B-Coder-Abliterated / README.md

Lord-H4D3ZS

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	---
	license: apache-2.0
	base_model: Qwen/Qwen3.6-35B-A3B
	tags:
	- qwen3
	- moe
	- a3b
	- mtp
	- gguf
	- rocmfpx
	- code
	pipeline_tag: text-generation
	---

	# Qwen3.8-Distill-35B-A3B-Coder-Abliterated (Q2 ROCmFPX, PoC)

	A 2-bit ROCmFPX GGUF of a distilled Qwen3.6-35B-A3B (MoE, 256 experts / ~3B active), sized to
	run on a 16GB consumer GPU. Ships with the MTP (`nextn`) head and build instructions for the
	matching runtime.

	> Honest status: this is a proof-of-concept. On the internal 10-task smoke eval the distilled
	> model tied its base (6/10 vs 6/10) — no regression, no measurable gain yet — and it is now
	> quantized to 2-bit, which trades quality for fit. Publishing it as a reproducible artifact of the
	> pipeline (distill → graft MTP → ROCmFPX 2-bit GGUF), not as a benchmark-winning coder.
	> The quality fix is a larger, tool-calling-heavy corpus — a separate follow-up run.

	## What this is

	- Base / architecture: `Qwen/Qwen3.6-35B-A3B` (`Qwen3_5MoeForCausalLM`, 256 experts, ~3B
	active). The "3.8" in the name refers to the teacher, not the base.
	- Teacher: abliterated `Qwen3.8-27B` (GGUF Q8_0) via llama.cpp — sequence-level reasoning
	distillation (teacher `<think>` chains as SFT targets).
	- Method: Unsloth 4-bit QLoRA, `completion_only_loss`, 1 epoch / 850 teacher completions,
	merged to bf16, MTP head grafted back from base, converted + quantized with ROCmFPX.
	- Quant (the interesting part): a hand-built role-aware mix — 2-bit experts
	(`Q2_0_ROCMFPX`, the ~90% bulk) + Q6 attention / embeddings / shared-experts / output
	(`Q6_0_ROCMFPX`, the coherence-critical ~10%), norms in F32. 12GB total, fits a 16GB card
	with ~4GB left for KV/context. This is the llama.cpp/ROCmFPX analogue of the eschamoe/OTQ
	role-aware idea: pure 2-bit-everywhere collapses the model; keeping attention precise while
	2-bit'ing the experts preserves coherence. See the exact `--tensor-type` recipe in
	[BUILD.md](BUILD.md).
	- "Abliterated": transferred over the training corpus (teacher was abliterated) — corpus-scoped,
	NOT a globally abliterated model.

	## Run it

	You need a `llama-server` built from the pinned ROCmFPX source — see [BUILD.md](BUILD.md).

	```bash
	llama-server -m *-Q2_ROCMFPX.gguf --host 127.0.0.1 --port 8080 \
	-ngl 99 -c 16384 -fa on --jinja --alias qwen38-distill-a3b
	# OpenAI-compatible API at http://127.0.0.1:8080/v1
	```

	16GB card: context and concurrency share one KV pool — pick single-stream long context
	(`-c 32768 -np 1`) or many short sessions (`-c 8192 -np 8`).

	## Known limitations (measured)

	- No accuracy gain over base yet; 2-bit lowers quality further.
	- Weak on tool-calling/agentic tasks (thin PoC corpus) — the first thing the next run must fix.
	- MTP `nextn` tensors are present but speculative decoding depends on your runtime's support
	(see BUILD.md). Text-only; no vision.

	## Files

	- `*-Q2_ROCMFPX.gguf` — the model (~16GB-card fit)
	- `BUILD.md` — build the ROCmFPX runtime (pinned commit `b2f5829`)
	- `build_rocmfpx.sh` — exact build script used

	Apache-2.0, inheriting the base model's terms.