Instructions to use Zwounds/boolean-search-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zwounds/boolean-search-model with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Zwounds/boolean-search-model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Zwounds/boolean-search-model")
model = AutoModelForCausalLM.from_pretrained("Zwounds/boolean-search-model", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Zwounds/boolean-search-model 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 Zwounds/boolean-search-model
# Run inference directly in the terminal:
llama cli -hf Zwounds/boolean-search-model

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Zwounds/boolean-search-model
# Run inference directly in the terminal:
llama cli -hf Zwounds/boolean-search-model

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 Zwounds/boolean-search-model
# Run inference directly in the terminal:
./llama-cli -hf Zwounds/boolean-search-model

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 Zwounds/boolean-search-model
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Zwounds/boolean-search-model

Use Docker

docker model run hf.co/Zwounds/boolean-search-model

LM Studio
Jan

vLLM

How to use Zwounds/boolean-search-model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Zwounds/boolean-search-model

SGLang

How to use Zwounds/boolean-search-model with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Zwounds/boolean-search-model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zwounds/boolean-search-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Zwounds/boolean-search-model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zwounds/boolean-search-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Zwounds/boolean-search-model with Ollama:
```
ollama run hf.co/Zwounds/boolean-search-model
```
Unsloth Desktop

How to use Zwounds/boolean-search-model with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Zwounds/boolean-search-model

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": "Zwounds/boolean-search-model"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Zwounds/boolean-search-model with Docker Model Runner:
```
docker model run hf.co/Zwounds/boolean-search-model
```

Lemonade

How to use Zwounds/boolean-search-model with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Zwounds/boolean-search-model

Run and chat with the model

lemonade run user.boolean-search-model-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Zwounds/boolean-search-model with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Zwounds/boolean-search-model

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 Zwounds/boolean-search-model

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Zwounds/boolean-search-model with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Zwounds/boolean-search-model

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 "Zwounds/boolean-search-model" \
  --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"

Zwounds commited on Mar 18, 2025

Commit

9690e7d

verified ·

1 Parent(s): 5499639

Upload model trained with Unsloth

Browse files

Upload model trained with Unsloth 2x faster

Files changed (6) hide show

README.md +6 -5
config.json +2 -2
generation_config.json +5 -2
model-00001-of-00002.safetensors +2 -2
model-00002-of-00002.safetensors +1 -1
model.safetensors.index.json +1 -1

README.md CHANGED Viewed

@@ -1,10 +1,11 @@
 ---
 tags:
-  - transformers
-  - llama
-  - boolean-search
-  - search
-  - language-to-query
 library_name: transformers
 pipeline_tag: text2text-generation
 license: llama2

 ---
 tags:
+- transformers
+- llama
+- boolean-search
+- search
+- language-to-query
+- unsloth
 library_name: transformers
 pipeline_tag: text2text-generation
 license: llama2

config.json CHANGED Viewed

@@ -6,7 +6,7 @@
   "attention_bias": false,
   "attention_dropout": 0.0,
   "bos_token_id": 128000,
-  "eos_token_id": 128001,
   "head_dim": 128,
   "hidden_act": "silu",
   "hidden_size": 4096,
@@ -18,7 +18,7 @@
   "num_attention_heads": 32,
   "num_hidden_layers": 32,
   "num_key_value_heads": 8,
-  "pad_token_id": 128002,
   "pretraining_tp": 1,
   "quantization_config": {
     "bnb_4bit_compute_dtype": "bfloat16",

   "attention_bias": false,
   "attention_dropout": 0.0,
   "bos_token_id": 128000,
+  "eos_token_id": 128009,
   "head_dim": 128,
   "hidden_act": "silu",
   "hidden_size": 4096,
   "num_attention_heads": 32,
   "num_hidden_layers": 32,
   "num_key_value_heads": 8,
+  "pad_token_id": 128004,
   "pretraining_tp": 1,
   "quantization_config": {
     "bnb_4bit_compute_dtype": "bfloat16",

generation_config.json CHANGED Viewed

@@ -1,8 +1,11 @@
 {
-  "_from_model_config": true,
   "bos_token_id": 128000,
   "do_sample": true,
-  "eos_token_id": 128001,
   "max_length": 131072,
   "pad_token_id": 128004,
   "temperature": 0.6,

 {
   "bos_token_id": 128000,
   "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
   "max_length": 131072,
   "pad_token_id": 128004,
   "temperature": 0.6,

model-00001-of-00002.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9d75e577a3d3e8ca6f0843d92b724ab8997594c314d08571e6bd7ccb7c75c00e
-size 4652073121

 version https://git-lfs.github.com/spec/v1
+oid sha256:b15a4b0bc7157bfadc78bb86dcaf9116f3ebcae6d819ac947017e595210a6522
+size 4652073135

model-00002-of-00002.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6feef628d893df7809d975f4c3a109a655e5e4126ac4ee28e8e0464504c86b12
 size 1050673280

 version https://git-lfs.github.com/spec/v1
+oid sha256:5321ca97dd0968a3e2b676b3a42728e9f5327d516bcf0addd4e9cd61c068a1ad
 size 1050673280

model.safetensors.index.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "metadata": {
-    "total_size": 5702577937
   },
   "weight_map": {
     "lm_head.weight": "model-00002-of-00002.safetensors",

 {
   "metadata": {
+    "total_size": 5702577951
   },
   "weight_map": {
     "lm_head.weight": "model-00002-of-00002.safetensors",