Instructions to use pkloats/qwen3-1.7b-bishop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pkloats/qwen3-1.7b-bishop with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-1.7b-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "pkloats/qwen3-1.7b-bishop")

llama-cpp-python

How to use pkloats/qwen3-1.7b-bishop with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pkloats/qwen3-1.7b-bishop",
	filename="checkpoints/qwen3-1.7b-bishop.stage1.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use pkloats/qwen3-1.7b-bishop 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 pkloats/qwen3-1.7b-bishop:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pkloats/qwen3-1.7b-bishop:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/pkloats/qwen3-1.7b-bishop:Q4_K_M

LM Studio
Jan

vLLM

How to use pkloats/qwen3-1.7b-bishop with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pkloats/qwen3-1.7b-bishop"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pkloats/qwen3-1.7b-bishop",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pkloats/qwen3-1.7b-bishop:Q4_K_M

Ollama
How to use pkloats/qwen3-1.7b-bishop with Ollama:
```
ollama run hf.co/pkloats/qwen3-1.7b-bishop:Q4_K_M
```

Unsloth Studio

How to use pkloats/qwen3-1.7b-bishop with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for pkloats/qwen3-1.7b-bishop to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for pkloats/qwen3-1.7b-bishop to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pkloats/qwen3-1.7b-bishop to start chatting

How to use pkloats/qwen3-1.7b-bishop with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pkloats/qwen3-1.7b-bishop:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "pkloats/qwen3-1.7b-bishop:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use pkloats/qwen3-1.7b-bishop with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pkloats/qwen3-1.7b-bishop: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 pkloats/qwen3-1.7b-bishop:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use pkloats/qwen3-1.7b-bishop with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pkloats/qwen3-1.7b-bishop: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 "pkloats/qwen3-1.7b-bishop: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"

Docker Model Runner
How to use pkloats/qwen3-1.7b-bishop with Docker Model Runner:
```
docker model run hf.co/pkloats/qwen3-1.7b-bishop:Q4_K_M
```

Lemonade

How to use pkloats/qwen3-1.7b-bishop with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pkloats/qwen3-1.7b-bishop:Q4_K_M

Run and chat with the model

lemonade run user.qwen3-1.7b-bishop-Q4_K_M

List all available models

lemonade list

Qwen3-1.7B · Bishop Chess Concepts (LoRA + GGUF)

A LoRA fine-tune of Qwen3-1.7B specialized on bishop concepts and strategy in chess (annotated games, study prose, and Q&A). This repo ships both the PEFT LoRA adapter and a quantized GGUF you can run directly in Ollama / llama.cpp.

Base model: unsloth/qwen3-1.7b-unsloth-bnb-4bit (Qwen3-1.7B)
Method: LoRA SFT via Unsloth + TRL
Adapter config: r=16, lora_alpha=16, lora_dropout=0, targets q,k,v,o,gate,up,down
Training data: pkloats/bishop-chess-dataset (~18.6M tokens)
PEFT: 0.19.1

Files

File	What it is
`adapter_model.safetensors`, `adapter_config.json`	the LoRA adapter (apply on top of the base model)
`tokenizer.json`, `tokenizer_config.json`, `chat_template.jinja`	tokenizer + chat template
`qwen3-1.7b-bishop.Q4_K_M.gguf`	release GGUF (stage3c) — merged + quantized, runnable standalone
`checkpoints/*.Q4_K_M.gguf`	intermediate training-progression checkpoints (see below)

Use the GGUF (Ollama)

ollama run hf.co/pkloats/qwen3-1.7b-bishop

Or with llama.cpp:

llama-cli -hf pkloats/qwen3-1.7b-bishop --file qwen3-1.7b-bishop.Q4_K_M.gguf -p "..."

Training-progression checkpoints

The checkpoints/ folder holds the intermediate GGUFs from the training run, so you can compare stages:

File	Stage
`checkpoints/qwen3-1.7b-bishop.stage1.Q4_K_M.gguf`	stage 1
`checkpoints/qwen3-1.7b-bishop.stage2.Q4_K_M.gguf`	stage 2
`checkpoints/qwen3-1.7b-bishop.stage3a.Q4_K_M.gguf`	stage 3a
`checkpoints/qwen3-1.7b-bishop.stage3b.Q4_K_M.gguf`	stage 3b
`qwen3-1.7b-bishop.Q4_K_M.gguf` (root)	stage 3c — released

All checkpoints are the same Q4_K_M quant, so the bare ollama run hf.co/pkloats/qwen3-1.7b-bishop always resolves to the root release. To try a specific stage, download it directly and point a runtime at the file:

hf download pkloats/qwen3-1.7b-bishop checkpoints/qwen3-1.7b-bishop.stage1.Q4_K_M.gguf --local-dir .
llama-cli --file checkpoints/qwen3-1.7b-bishop.stage1.Q4_K_M.gguf -p "..."

Use the LoRA adapter (PEFT)

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B", device_map="auto")
model = PeftModel.from_pretrained(base, "pkloats/qwen3-1.7b-bishop")
tok = AutoTokenizer.from_pretrained("pkloats/qwen3-1.7b-bishop")

Intended use & limitations

Intended for chess study/analysis text generation, with emphasis on bishop-related ideas. It is a 1.7B model and will hallucinate illegal moves or incorrect evaluations; do not treat its output as an engine. Training data provenance is mixed (see the dataset card) — released for research use.

Framework versions

PEFT 0.19.1

Downloads last month: -

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

4-bit

pkloats
/

qwen3-1.7b-bishop

Qwen3-1.7B · Bishop Chess Concepts (LoRA + GGUF)

Files

Use the GGUF (Ollama)

Training-progression checkpoints

Use the LoRA adapter (PEFT)

Intended use & limitations

Framework versions

Dataset used to train pkloats/qwen3-1.7b-bishop