Instructions to use cstr/index-echo-9b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use cstr/index-echo-9b-GGUF 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 cstr/index-echo-9b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cstr/index-echo-9b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cstr/index-echo-9b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cstr/index-echo-9b-GGUF: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 cstr/index-echo-9b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cstr/index-echo-9b-GGUF: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 cstr/index-echo-9b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cstr/index-echo-9b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/cstr/index-echo-9b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use cstr/index-echo-9b-GGUF with Ollama:
```
ollama run hf.co/cstr/index-echo-9b-GGUF:Q4_K_M
```
Unsloth Desktop

How to use cstr/index-echo-9b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cstr/index-echo-9b-GGUF: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": "cstr/index-echo-9b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use cstr/index-echo-9b-GGUF with Docker Model Runner:
```
docker model run hf.co/cstr/index-echo-9b-GGUF:Q4_K_M
```

Lemonade

How to use cstr/index-echo-9b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cstr/index-echo-9b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.index-echo-9b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use cstr/index-echo-9b-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cstr/index-echo-9b-GGUF: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 cstr/index-echo-9b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use cstr/index-echo-9b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cstr/index-echo-9b-GGUF: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 "cstr/index-echo-9b-GGUF: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"

Index-Echo S2TT 9B — CrispASR GGUF

Native speech translation with bilingual, timestamped subtitles, primarily Chinese → English, Japanese or Spanish. This repository contains the validated F16 encoder/connector and decoder pair for CrispASR.

File	Bytes	GiB
`index-echo-9b-f16.gguf`	1,313,848,736	1.224
`index-echo-9b-decoder-f16.gguf`	17,920,696,992	16.690
Pair	19,234,545,728	17.914

Use

Keep both GGUF files in the same directory. Use a CrispASR build containing the 9B integration; earlier 2B-only builds cannot load this pair.

crispasr -m index-echo-9b-f16.gguf --auto-download -f speech.wav --target-lang en -osrt

--target-lang selects en, ja or es. Complete-file processing also uses the standard ggml-silero-v6.2.0.bin VAD companion; --auto-download fetches it. The 2B model remains CrispASR's smaller default. This 9B repository supports F16 only.

Memory and validation

Allow about 18 GiB for weights plus working memory. CPU inference and CUDA inference were validated; the CUDA runs used two 15 GiB Tesla T4 cards. Host RAM requirements also depend on model loading and mmap behavior.

The F16 pair passes focused original-reference stage, decoded-output and timestamp controls on CPU and CUDA, including speech roundtrips. Immutable pins, hashes and evidence are in acceptance.json. Detailed results, rejected experiments and profiling measurements are archived in CrispASR's history.

Provenance and license

Source: IndexTeam/Index-Echo-S2TT-9B, revision b8ac6fb7d3dc17cee48a52201bd3d93dc86b0dba. Decoder conversion: ggml-org/llama.cpp@42d958167a748f2c04b1f888e84e7a58f609ddcb, F16 with --no-mtp.

Apache-2.0; LICENSE is copied from the pinned upstream repository.

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GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

16-bit

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