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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metadata

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.
"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.

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.