---
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
```python
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
```bash
vllm serve ertghiu256/qwen3-1.7b-mixture-of-thought --enable-reasoning --reasoning-parser deepseek_r1
```

## Sglang
Run this command
```bash
python -m sglang.launch_server --model-path ertghiu256/qwen3-1.7b-mixture-of-thought --reasoning-parser deepseek-r1
```

## llama.cpp
Run this command
```bash
llama-server --hf-repo ertghiu256/qwen3-1.7b-mixture-of-thought
```
or
```bash
llama-cli --hf ertghiu256/qwen3-1.7b-mixture-of-thought
```

## ollama
Run this command 
```bash
ollama run hf.co/ertghiu256/qwen3-1.7b-mixture-of-thought:Q4_K_M 
```

## lm studio 
Search 
```
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
```