metadata

license: apache-2.0
datasets:
  - mlabonne/FineTome-100k
  - open-r1/Mixture-of-Thoughts
base_model:
  - Qwen/Qwen3-1.7B
tags:
  - think
  - reasoning
  - qwen3

Model details

This is a Qwen 3 1.7b model trained on 20k conversations from open-r1/Mixture-of-Thoughts and 3k conversations from mlabonne/FineTome-100k to enchance it's reasoning capabilities. This model aims to run in weaker or old devices such as smartphones or an old laptop.

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/qwen3-1.7b-mixture-of-thought"

# 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/qwen3-1.7b-mixture-of-thought --enable-reasoning --reasoning-parser deepseek_r1

Sglang

Run this command

python -m sglang.launch_server --model-path ertghiu256/qwen3-1.7b-mixture-of-thought --reasoning-parser deepseek-r1

llama.cpp

Run this command

llama-server --hf-repo ertghiu256/qwen3-1.7b-mixture-of-thought

llama-cli --hf ertghiu256/qwen3-1.7b-mixture-of-thought

ollama

Run this command

ollama run hf.co/ertghiu256/qwen3-1.7b-mixture-of-thought:Q4_K_M

lm studio

ertghiu256/qwen3-1.7b-mixture-of-thought

in the lm studio model search list then download

Recomended parameters

Extended thinking mode

temp: 0.6
num_ctx: ≥8192
top_p: 0.95
top_k: 10

Short thinking mode

temp: 0.5
num_ctx: ≥4096
top_p: 0.8
top_k: 10
min_p: 0.1

Training details

Lora rank: 32
Learning rate: 1e-4
Steps: 70
Datasets:
  - FlameF0X/Mixture-of-Thoughts-2048T
  - mlabonne/FineTome-100k