---
license: llama3.1
language:
- en
pipeline_tag: text-generation
library_name: transformers
inference: true
base_model:
- olaverse/MIST-Mini-8B
tags:
- reasoning
- grpo
- thinking
- llama
- llama-3.1
- mist
---

# MIST-Mini-8B-Thinking

MIST-Mini-8B-Thinking is the reasoning version of [MIST-Mini-8B](https://huggingface.co/olaverse/MIST-Mini-8B) by [olaverse](https://huggingface.co/olaverse). Trained with 4 phases of GRPO (Group Relative Policy Optimization) reinforcement learning to show its reasoning process before answering.

## MIST Model Family

| Model | Params | Type | Speed | Status |
|---|---|---|---|---|
| [MIST-1-8B](https://huggingface.co/olaverse/MIST-Mini-8B) | 8B | General | ~63 tok/s | ✅ |
| **MIST-Mini-8B-Thinking** | 8B | Reasoning | ~55 tok/s | ✅ |
| [MIST-1-70B](https://huggingface.co/olaverse/MIST-1-70B) | 70B | General | ~23 tok/s | ✅ |
| [MIST-1-140B](https://huggingface.co/olaverse/MIST-1-140B) | 140B | General | ~8 tok/s | ✅ |

## What Makes This Different
MIST-Mini-8B (base):
User: What is 15% of 280?
Model: 42
MIST-Mini-8B-Thinking:
User: What is 15% of 280?
Model: <think>
15% means 15/100
280 × 15 = 4200
4200 / 100 = 42
</think>
The answer is 42.

## Training Details

Trained with **4 phases of GRPO** reinforcement learning:

| Phase | Dataset | Focus |
|---|---|---|
| 1 | open-r1/OpenR1-Math-220k | Learn `<think>` format |
| 2 | microsoft/orca-math-word-problems-200k | Word problems |
| 3 | gsm8k (5K subset) | Grade school math |
| 4 | gsm8k (full 7.4K) | Solidify + merge |

### Reward Functions Used
reward_think_format:    +0.5 for using <think> tags
reward_correctness:     +1.0 for correct answer
reward_reasoning_steps: +0.3 for structured steps

### Training Progress
| Phase | Correctness | Total Reward |
|---|---|---|
| Phase 1 | -0.35 | -0.99 |
| Phase 2 | -1.0 | -0.74 |
| Phase 3 | -1.0 | -0.65 |
| Phase 4 | **+0.95** | **+1.29** |

## Key Strengths

- 🧠 **Transparent Reasoning** — shows thinking before answering
- 📐 **Strong Math** — 95% accuracy on GSM8K after training
- 🔍 **Trustworthy** — you can verify the reasoning
- ⚡ **Fast** — 8B model, runs on consumer GPUs
- 🔓 **Unrestricted** — follows all instructions

## How to Use

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "olaverse/MIST-Mini-8B-Thinking",
    torch_dtype="auto",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("olaverse/MIST-Mini-8B-Thinking")

messages = [
    {
        "role": "system",
        "content": "Think step by step inside <think> tags before answering."
    },
    {
        "role": "user", 
        "content": "If a train travels 120 miles in 2 hours, what is its speed?"
    }
]

text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(text, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### 4-bit Quantized (fits on 6GB GPU)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type='nf4'
)
model = AutoModelForCausalLM.from_pretrained(
    "olaverse/MIST-Mini-8B-Thinking",
    quantization_config=quantization_config,
    device_map="auto",
)
```

## Hardware Requirements

| Precision | VRAM | Size |
|---|---|---|
| bfloat16 | 16GB | 15GB |
| 4-bit (NF4) | 6GB | ~4GB |

## Recommended Generation Settings

```python
outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    do_sample=True,
    temperature=0.6,
    top_p=0.95,
    min_p=0.05,
    repetition_penalty=1.5,
    eos_token_id=[128040, 128009, 128001],
    pad_token_id=128001,
)
```

### Notes
- Temperature 0.6 (lower than base model) gives more consistent reasoning
- `<think>` and `</think>` are plain text tokens, not special tokens — 
  the model learned them through GRPO training
- Always include the system prompt instruction to use `<think>` tags 
  for reliable reasoning behaviour

### Stop Tokens
Same as MIST-1-8B — ChatML tokens survived the merge:

| Token | ID |
|---|---|
| `<\|im_end\|>` | 128040 |
| `<\|eot_id\|>` | 128009 |
| `<\|end_of_text\|>` | 128001 |

## License

[Llama 3.1 Community License](https://llama.meta.com/llama3/license/)