Qwen 3 Finetuned
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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.
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)
Run this command
vllm serve ertghiu256/Qwen3-Hermes-4b --enable-reasoning --reasoning-parser deepseek_r1
Run this command
python -m sglang.launch_server --model-path ertghiu256/Qwen3-Hermes-4b --reasoning-parser deepseek-r1
Run this command
llama-server --hf-repo ertghiu256/Qwen3-Hermes-4b
or
llama-cli --hf ertghiu256/Qwen3-Hermes-4b
Run this command
ollama run hf.co/ertghiu256/Qwen3-Hermes-4b:Q4_K_M
Search
ertghiu256/Qwen3-Hermes-4b
in the lm studio model search list then download