Safetensors
GGUF
qwen3
conversational
How to use from
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-Hermes-4b:
# Run inference directly in the terminal:
llama cli -hf ertghiu256/Qwen3-Hermes-4b:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ertghiu256/Qwen3-Hermes-4b:
# Run inference directly in the terminal:
llama cli -hf ertghiu256/Qwen3-Hermes-4b:
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-Hermes-4b:
# Run inference directly in the terminal:
./llama-cli -hf ertghiu256/Qwen3-Hermes-4b:
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-Hermes-4b:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ertghiu256/Qwen3-Hermes-4b:
Use Docker
docker model run hf.co/ertghiu256/Qwen3-Hermes-4b:
Quick Links

This Qwen 3 4B model was fine-tuned on the Hermes 3 dataset to enhance its general chatting capabilities while retaining Qwen's Reasoning capabilities.

transformers

As the qwen team suggested to use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ertghiu256/Qwen3-Hermes-4b"

# 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,
    enable_thinking=True # Switches between thinking and non-thinking modes. Default is 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)
print("content:", content)

vllm

Run this command

vllm serve ertghiu256/Qwen3-Hermes-4b --enable-reasoning --reasoning-parser deepseek_r1

Sglang

Run this command

python -m sglang.launch_server --model-path ertghiu256/Qwen3-Hermes-4b --reasoning-parser deepseek-r1

llama.cpp

Run this command

llama-server --hf-repo ertghiu256/Qwen3-Hermes-4b

or

llama-cli --hf ertghiu256/Qwen3-Hermes-4b

ollama

Run this command

ollama run hf.co/ertghiu256/Qwen3-Hermes-4b:Q4_K_M 

lm studio

Search

ertghiu256/Qwen3-Hermes-4b

in the lm studio model search list then download

Downloads last month
272
Safetensors
Model size
4B params
Tensor type
BF16
·
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for ertghiu256/Qwen3-Hermes-4b

Finetuned
Qwen/Qwen3-4B
Quantized
(315)
this model
Merges
7 models
Quantizations
1 model

Datasets used to train ertghiu256/Qwen3-Hermes-4b

Collection including ertghiu256/Qwen3-Hermes-4b