Instructions to use iteratehack/sam-qwen3-1.7b-sams-v7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iteratehack/sam-qwen3-1.7b-sams-v7 with Transformers:

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("iteratehack/sam-qwen3-1.7b-sams-v7", device_map="auto")

PEFT
How to use iteratehack/sam-qwen3-1.7b-sams-v7 with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use iteratehack/sam-qwen3-1.7b-sams-v7 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 iteratehack/sam-qwen3-1.7b-sams-v7:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf iteratehack/sam-qwen3-1.7b-sams-v7:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf iteratehack/sam-qwen3-1.7b-sams-v7:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf iteratehack/sam-qwen3-1.7b-sams-v7: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 iteratehack/sam-qwen3-1.7b-sams-v7:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf iteratehack/sam-qwen3-1.7b-sams-v7: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 iteratehack/sam-qwen3-1.7b-sams-v7:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf iteratehack/sam-qwen3-1.7b-sams-v7:Q4_K_M

Use Docker

docker model run hf.co/iteratehack/sam-qwen3-1.7b-sams-v7:Q4_K_M

LM Studio
Jan
Ollama
How to use iteratehack/sam-qwen3-1.7b-sams-v7 with Ollama:
```
ollama run hf.co/iteratehack/sam-qwen3-1.7b-sams-v7:Q4_K_M
```
Unsloth Desktop

How to use iteratehack/sam-qwen3-1.7b-sams-v7 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf iteratehack/sam-qwen3-1.7b-sams-v7: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": "iteratehack/sam-qwen3-1.7b-sams-v7:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use iteratehack/sam-qwen3-1.7b-sams-v7 with Docker Model Runner:
```
docker model run hf.co/iteratehack/sam-qwen3-1.7b-sams-v7:Q4_K_M
```

Lemonade

How to use iteratehack/sam-qwen3-1.7b-sams-v7 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull iteratehack/sam-qwen3-1.7b-sams-v7:Q4_K_M

Run and chat with the model

lemonade run user.sam-qwen3-1.7b-sams-v7-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use iteratehack/sam-qwen3-1.7b-sams-v7 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf iteratehack/sam-qwen3-1.7b-sams-v7: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 iteratehack/sam-qwen3-1.7b-sams-v7:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use iteratehack/sam-qwen3-1.7b-sams-v7 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf iteratehack/sam-qwen3-1.7b-sams-v7: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 "iteratehack/sam-qwen3-1.7b-sams-v7: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"

SAMS Qwen3-1.7B approved-wording selector v7

This release was trained from the official Qwen/Qwen3-1.7B safetensors checkpoint using 4-bit NF4 QLoRA with BF16 compute. The adapter was merged into the official base before GGUF Q4_K_M and Q5_K_M quantization.

Strict frozen release gate: Q4_K_M 40/40; Q5_K_M 40/40. Every supplied wording is prevalidated for grounding and safety before inference; the model chooses style among four approved paraphrases. The JSON schema allows all supplied IDs and does not contain a hidden expected answer. A score of 39/40 is rejected.

The model selects one supplied approved natural-language response. The deterministic controller supplies the question, workflow action, routing flags, and cited facts. Low-confidence speech, fixed warnings, medical triage, emergency/descent decisions, and robot motion, navigation, motor, joint, and power control bypass this model.

Important: the targets are source-grounded synthetic drafts requiring domain review. The 40-case result is a bounded software-contract regression result, not clinical, medical-device, mountaineering-safety, or robot-safety validation. Generic robot fields are not real Unitree G1 Himalayan logs.

Downloads last month: 15

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

4-bit

5-bit

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Model tree for iteratehack/sam-qwen3-1.7b-sams-v7

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