Instructions to use ertghiu256/Qwen3-4b-tcomanr-merge-v2.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ertghiu256/Qwen3-4b-tcomanr-merge-v2.3 with Transformers:

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

pipe = pipeline("text-generation", model="ertghiu256/Qwen3-4b-tcomanr-merge-v2.3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ertghiu256/Qwen3-4b-tcomanr-merge-v2.3")
model = AutoModelForCausalLM.from_pretrained("ertghiu256/Qwen3-4b-tcomanr-merge-v2.3", 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]:]))

llama-cpp-python

How to use ertghiu256/Qwen3-4b-tcomanr-merge-v2.3 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ertghiu256/Qwen3-4b-tcomanr-merge-v2.3",
	filename="Tcomanr-V2_3-4.0B-IQ4_NL.gguf",
)

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ertghiu256/Qwen3-4b-tcomanr-merge-v2.3 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 ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL
# Run inference directly in the terminal:
llama cli -hf ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL
# Run inference directly in the terminal:
llama cli -hf ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL

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 ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL
# Run inference directly in the terminal:
./llama-cli -hf ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL

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 ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL

Use Docker

docker model run hf.co/ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL

LM Studio
Jan

vLLM

How to use ertghiu256/Qwen3-4b-tcomanr-merge-v2.3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ertghiu256/Qwen3-4b-tcomanr-merge-v2.3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ertghiu256/Qwen3-4b-tcomanr-merge-v2.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL

SGLang

How to use ertghiu256/Qwen3-4b-tcomanr-merge-v2.3 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 "ertghiu256/Qwen3-4b-tcomanr-merge-v2.3" \
    --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": "ertghiu256/Qwen3-4b-tcomanr-merge-v2.3",
		"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 "ertghiu256/Qwen3-4b-tcomanr-merge-v2.3" \
        --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": "ertghiu256/Qwen3-4b-tcomanr-merge-v2.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ertghiu256/Qwen3-4b-tcomanr-merge-v2.3 with Ollama:
```
ollama run hf.co/ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL
```

Unsloth Studio

How to use ertghiu256/Qwen3-4b-tcomanr-merge-v2.3 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 ertghiu256/Qwen3-4b-tcomanr-merge-v2.3 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 ertghiu256/Qwen3-4b-tcomanr-merge-v2.3 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ertghiu256/Qwen3-4b-tcomanr-merge-v2.3 to start chatting

How to use ertghiu256/Qwen3-4b-tcomanr-merge-v2.3 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL

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": "ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ertghiu256/Qwen3-4b-tcomanr-merge-v2.3 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL

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 ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ertghiu256/Qwen3-4b-tcomanr-merge-v2.3 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL

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 "ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL" \
  --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 ertghiu256/Qwen3-4b-tcomanr-merge-v2.3 with Docker Model Runner:
```
docker model run hf.co/ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL
```

Lemonade

How to use ertghiu256/Qwen3-4b-tcomanr-merge-v2.3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL

Run and chat with the model

lemonade run user.Qwen3-4b-tcomanr-merge-v2.3-IQ4_NL

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

A newer version of this model is available: ertghiu256/Qwen3-4b-tcomanr-merge-v2.5

Ties merged COde MAth aNd Reasoning model

This is a merge of pre-trained language models created using mergekit.

Merge Details

This model is a revision of the ertghiu256/Qwen3-4b-tcomanr-merge-v2.2

This model aims to combine the reasoning, code, and math capabilities of Qwen3 4b 2507 reasoning by merging it with some other Qwen3 finetunes. This model reasoning is very long.

How to run

You can run this model by using multiple interface choices

Transformers

As the qwen team suggested to use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ertghiu256/Qwen3-4b-tcomanr-merge-v2.3"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

# parsing thinking content
try:
    # rindex finding 151668 (</think>)
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0

thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")

print("thinking content:", thinking_content) # no opening <think> tag
print("content:", content)

Vllm

Run this command

vllm serve ertghiu256/Qwen3-4b-tcomanr-merge-v2.3 --enable-reasoning --reasoning-parser deepseek_r1

Sglang

Run this command

python -m sglang.launch_server --model-path ertghiu256/Qwen3-4b-tcomanr-merge-v2.3 --reasoning-parser deepseek-r1

llama.cpp

Run this command

llama-server --hf-repo ertghiu256/Qwen3-4b-tcomanr-merge-v2.3

llama-cli --hf ertghiu256/Qwen3-4b-tcomanr-merge-v2.3

Ollama

View the model at ollama.com or Run this command

ollama run ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:Q8_0

or for Q5_K_M quant

ollama run ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:Q5_K_M

or for IQ4_NL quant

ollama run ertghiu256/Qwen3-4b-tcomanr-merge-v2.3:IQ4_NL

LM Studio

ertghiu256/Qwen3-4b-tcomanr-merge-v2.3

in the lm studio model search list then download

Recomended parameters

temp: 0.6
num_ctx: ≥8192
top_p: 0.95
top_k: 10
Repeat Penalty: 1.1

Merge Method

This model was merged using the TIES merge method using Qwen/Qwen3-4B-Thinking-2507 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: ertghiu256/qwen3-math-reasoner
    parameters:
      weight: 0.8
  - model: ertghiu256/qwen3-4b-code-reasoning
    parameters:
      weight: 0.9
  - model: ertghiu256/qwen-3-4b-mixture-of-thought
    parameters:
      weight: 1.0
  - model: POLARIS-Project/Polaris-4B-Preview
    parameters:
      weight: 0.8
  - model: ertghiu256/qwen3-multi-reasoner
    parameters:
      weight: 0.9
  - model: ertghiu256/Qwen3-Hermes-4b
    parameters:
      weight: 0.7
  - model: ValiantLabs/Qwen3-4B-Esper3
    parameters:
      weight: 0.75
  - model: Tesslate/UIGEN-T3-4B-Preview-MAX
    parameters:
      weight: 1.0
  - model: ValiantLabs/Qwen3-4B-ShiningValiant3
    parameters:
      weight: 0.6
      density: 0.5
  - model: huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated
    parameters:
      weight: 0.75
  - model: Qwen/Qwen3-4B-Thinking-2507
    parameters:
      weight: 1.0
  - model: Qwen/Qwen3-4b-Instruct-2507
    parameters:
      weight: 0.75
  - model: GetSoloTech/Qwen3-Code-Reasoning-4B
    parameters:
      weight: 0.75
      density: 0.55
  - model: ertghiu256/Qwen3-4B-Thinking-2507-Hermes-3
    parameters:
      weight: 1.0
  - model: janhq/Jan-v1-4B
    parameters:
      weight: 0.3
merge_method: ties
base_model: Qwen/Qwen3-4B-Thinking-2507
parameters:
  normalize: true
  int8_mask: true
  lambda: 1.0
dtype: float16