Instructions to use cafonez/Escha-W2-35B-A3B-ROCmFP2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use cafonez/Escha-W2-35B-A3B-ROCmFP2 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 cafonez/Escha-W2-35B-A3B-ROCmFP2
# Run inference directly in the terminal:
llama cli -hf cafonez/Escha-W2-35B-A3B-ROCmFP2

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cafonez/Escha-W2-35B-A3B-ROCmFP2
# Run inference directly in the terminal:
llama cli -hf cafonez/Escha-W2-35B-A3B-ROCmFP2

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 cafonez/Escha-W2-35B-A3B-ROCmFP2
# Run inference directly in the terminal:
./llama-cli -hf cafonez/Escha-W2-35B-A3B-ROCmFP2

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 cafonez/Escha-W2-35B-A3B-ROCmFP2
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cafonez/Escha-W2-35B-A3B-ROCmFP2

Use Docker

docker model run hf.co/cafonez/Escha-W2-35B-A3B-ROCmFP2

LM Studio
Jan

vLLM

How to use cafonez/Escha-W2-35B-A3B-ROCmFP2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cafonez/Escha-W2-35B-A3B-ROCmFP2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cafonez/Escha-W2-35B-A3B-ROCmFP2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cafonez/Escha-W2-35B-A3B-ROCmFP2

Ollama
How to use cafonez/Escha-W2-35B-A3B-ROCmFP2 with Ollama:
```
ollama run hf.co/cafonez/Escha-W2-35B-A3B-ROCmFP2
```
Unsloth Desktop

How to use cafonez/Escha-W2-35B-A3B-ROCmFP2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cafonez/Escha-W2-35B-A3B-ROCmFP2

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": "cafonez/Escha-W2-35B-A3B-ROCmFP2"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use cafonez/Escha-W2-35B-A3B-ROCmFP2 with Docker Model Runner:
```
docker model run hf.co/cafonez/Escha-W2-35B-A3B-ROCmFP2
```

Lemonade

How to use cafonez/Escha-W2-35B-A3B-ROCmFP2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cafonez/Escha-W2-35B-A3B-ROCmFP2

Run and chat with the model

lemonade run user.Escha-W2-35B-A3B-ROCmFP2-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use cafonez/Escha-W2-35B-A3B-ROCmFP2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cafonez/Escha-W2-35B-A3B-ROCmFP2

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 cafonez/Escha-W2-35B-A3B-ROCmFP2

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use cafonez/Escha-W2-35B-A3B-ROCmFP2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cafonez/Escha-W2-35B-A3B-ROCmFP2

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 "cafonez/Escha-W2-35B-A3B-ROCmFP2" \
  --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"

cafonez commited on 29 days ago

Commit

eb73f4d

verified ·

1 Parent(s): 40d450f

Confirm reproducibility: clean main build matches dev build performance

Browse files

Files changed (1) hide show

README.md +5 -6

README.md CHANGED Viewed

@@ -145,13 +145,12 @@ Two things worth internalizing:
 **Strict verification is nearly free** (95.5 vs 96.1 t/s, ~0.5%) and guarantees output
 identical to non-speculative greedy decoding. Leave it on.
-### Build provenance (please read before comparing numbers)
-Measurements were taken with a build carrying some local modifications on top of the
-public tree. The ROCmFP2 Vulkan MMVQ fast path — the kernel that determines decode
-throughput for this quantization — **is** on `main`, so a current clean clone should
-reach comparable numbers. It is still not a bit-for-bit reproduction of the benchmarking
-binary, so treat the tables as indicative rather than exact, and see the variance note
 below before drawing conclusions from small differences.
 ### Throughput

 **Strict verification is nearly free** (95.5 vs 96.1 t/s, ~0.5%) and guarantees output
 identical to non-speculative greedy decoding. Leave it on.
+### Reproducibility
+A clean build of `main` was benchmarked against the development build on the same
+model and flags, and they are indistinguishable — pp512 1252 vs 1256 t/s, tg128
+65.11 vs 65.09 t/s, well inside run-to-run noise. **The public repo delivers the full
+ROCmFP2 performance**; nothing here depends on unpublished work. See the variance note
 below before drawing conclusions from small differences.
 ### Throughput