Instructions to use Misakachain/Qwen3.6-35B-A3B-PALW-runtime with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Misakachain/Qwen3.6-35B-A3B-PALW-runtime 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 Misakachain/Qwen3.6-35B-A3B-PALW-runtime:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Misakachain/Qwen3.6-35B-A3B-PALW-runtime:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Misakachain/Qwen3.6-35B-A3B-PALW-runtime:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Misakachain/Qwen3.6-35B-A3B-PALW-runtime:Q4_K_M

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 Misakachain/Qwen3.6-35B-A3B-PALW-runtime:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Misakachain/Qwen3.6-35B-A3B-PALW-runtime:Q4_K_M

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 Misakachain/Qwen3.6-35B-A3B-PALW-runtime:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Misakachain/Qwen3.6-35B-A3B-PALW-runtime:Q4_K_M

Use Docker

docker model run hf.co/Misakachain/Qwen3.6-35B-A3B-PALW-runtime:Q4_K_M

LM Studio
Jan
Ollama
How to use Misakachain/Qwen3.6-35B-A3B-PALW-runtime with Ollama:
```
ollama run hf.co/Misakachain/Qwen3.6-35B-A3B-PALW-runtime:Q4_K_M
```
Unsloth Desktop

How to use Misakachain/Qwen3.6-35B-A3B-PALW-runtime with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Misakachain/Qwen3.6-35B-A3B-PALW-runtime:Q4_K_M

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": "Misakachain/Qwen3.6-35B-A3B-PALW-runtime:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Misakachain/Qwen3.6-35B-A3B-PALW-runtime with Docker Model Runner:
```
docker model run hf.co/Misakachain/Qwen3.6-35B-A3B-PALW-runtime:Q4_K_M
```

Lemonade

How to use Misakachain/Qwen3.6-35B-A3B-PALW-runtime with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Misakachain/Qwen3.6-35B-A3B-PALW-runtime:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-PALW-runtime-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Misakachain/Qwen3.6-35B-A3B-PALW-runtime with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Misakachain/Qwen3.6-35B-A3B-PALW-runtime:Q4_K_M

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 Misakachain/Qwen3.6-35B-A3B-PALW-runtime:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Misakachain/Qwen3.6-35B-A3B-PALW-runtime with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Misakachain/Qwen3.6-35B-A3B-PALW-runtime:Q4_K_M

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 "Misakachain/Qwen3.6-35B-A3B-PALW-runtime:Q4_K_M" \
  --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"

Misakachain commited on Jul 31

Commit

801ce70

verified ·

1 Parent(s): ee7d75c

node-bridge cross-provider certification verified from the LM Studio path (mirrors 4a7ad37)

Browse files

Files changed (2) hide show

docs/evidence/qi35-lmstudio-palw-receipt.md +18 -6
docs/evidence/qi35_lmstudio_gateway.sh +1 -0

docs/evidence/qi35-lmstudio-palw-receipt.md CHANGED Viewed

@@ -130,13 +130,25 @@ committed 1340-token prompt, `.search.json` `receipt_binding` and the
 opening's `context_meta.search` matching — then settled `certified` through
 the same replica pipeline.
 Not on the gateway path yet (stated honestly): model-emitted tool calls, and
-everything consensus-side (`--palw-loopback` certifies plumbing, not
-economics — point `QI35_PALW_COORDINATOR` at a node bridge for the real
-counterparty). Verifier note: `palw-verify-search` replays LEGACY openings
-(`external_input` form); gateway search turns verify today via the opening's
-ids + `.search.json` as above, and extending the Rust verifier to the
-`qi35-gateway-opening/v1` shape is follow-up work.
 ## Receipt and search semantics

 opening's `context_meta.search` matching — then settled `certified` through
 the same replica pipeline.
+The node-side coordinator works against this path end-to-end
+(verified 2026-07-31): `misaka-palw-bridge` on 26621, gateway A launched with
+`QI35_PALW_COORDINATOR=http://127.0.0.1:26621/palw/v1
+QI35_PALW_PROVIDER=prov-a-lmstudio`, plus a second gateway instance as
+`prov-b-replica` with its own real 35B engine process. Two `palw_mint` LM
+Studio turns were submitter≠replica certified by the bridge's real
+`ReplicaMatchKey`/`run_replica_k2` (output + route/kv/state roots): the
+bridge offered each job only to B, B's engine replayed it (cold ~50 s to
+certified, warm within seconds of the 8.3 s answer), journal seq 8 with both
+matches on B and zero on A. The next turn's `lmstudio_resolve` frame showed
+the bridge-certified parent inside LM Studio.
 Not on the gateway path yet (stated honestly): model-emitted tool calls, and
+the on-chain half of settlement (the bridge itself notes "dev harness mode —
+no challenges, bonds, DA or arbitration"; `--palw-loopback` remains the
+zero-setup default). Verifier note: `palw-verify-search` replays LEGACY
+openings (`external_input` form); gateway search turns verify today via the
+opening's ids + `.search.json` as above, and extending the Rust verifier to
+the `qi35-gateway-opening/v1` shape is follow-up work.
 ## Receipt and search semantics

docs/evidence/qi35_lmstudio_gateway.sh CHANGED Viewed

@@ -67,6 +67,7 @@ if [[ -n "${QI35_PALW_COORDINATOR:-}" ]]; then
   palw_args=(--palw-coordinator "$QI35_PALW_COORDINATOR")
   [[ -n "${QI35_PALW_COORDINATOR_TOKEN:-}" ]] && palw_args+=(--palw-coordinator-token "$QI35_PALW_COORDINATOR_TOKEN")
 fi
 [[ "${QI35_METAL:-v3}" == "0" ]] && palw_args+=(--no-metal)
 [[ "$QI35_SEARCH_AUTO" != "off" ]] && palw_args+=(--search-cmd "$SP/qi35_search_sidecar.py")

   palw_args=(--palw-coordinator "$QI35_PALW_COORDINATOR")
   [[ -n "${QI35_PALW_COORDINATOR_TOKEN:-}" ]] && palw_args+=(--palw-coordinator-token "$QI35_PALW_COORDINATOR_TOKEN")
 fi
+[[ -n "${QI35_PALW_PROVIDER:-}" ]] && palw_args+=(--palw-provider "$QI35_PALW_PROVIDER")
 [[ "${QI35_METAL:-v3}" == "0" ]] && palw_args+=(--no-metal)
 [[ "$QI35_SEARCH_AUTO" != "off" ]] && palw_args+=(--search-cmd "$SP/qi35_search_sidecar.py")