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Model: olaverse/MIST-Mini-8B-Thinking Source: Original Platform
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README.md
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README.md
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---
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license: llama3.1
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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inference: true
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base_model:
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- olaverse/MIST-Mini-8B
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tags:
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- reasoning
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- grpo
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- thinking
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- llama
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- llama-3.1
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- mist
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---
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# MIST-Mini-8B-Thinking
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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.
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## MIST Model Family
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| Model | Params | Type | Speed | Status |
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|---|---|---|---|---|
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| [MIST-1-8B](https://huggingface.co/olaverse/MIST-Mini-8B) | 8B | General | ~63 tok/s | ✅ |
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| **MIST-Mini-8B-Thinking** | 8B | Reasoning | ~55 tok/s | ✅ |
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| [MIST-1-70B](https://huggingface.co/olaverse/MIST-1-70B) | 70B | General | ~23 tok/s | ✅ |
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| [MIST-1-140B](https://huggingface.co/olaverse/MIST-1-140B) | 140B | General | ~8 tok/s | ✅ |
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## What Makes This Different
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MIST-Mini-8B (base):
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User: What is 15% of 280?
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Model: 42
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MIST-Mini-8B-Thinking:
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User: What is 15% of 280?
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Model: <think>
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15% means 15/100
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280 × 15 = 4200
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4200 / 100 = 42
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</think>
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The answer is 42.
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## Training Details
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Trained with **4 phases of GRPO** reinforcement learning:
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| Phase | Dataset | Focus |
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|---|---|---|
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| 1 | open-r1/OpenR1-Math-220k | Learn `<think>` format |
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| 2 | microsoft/orca-math-word-problems-200k | Word problems |
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| 3 | gsm8k (5K subset) | Grade school math |
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| 4 | gsm8k (full 7.4K) | Solidify + merge |
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### Reward Functions Used
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reward_think_format: +0.5 for using <think> tags
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reward_correctness: +1.0 for correct answer
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reward_reasoning_steps: +0.3 for structured steps
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### Training Progress
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| Phase | Correctness | Total Reward |
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|---|---|---|
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| Phase 1 | -0.35 | -0.99 |
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| Phase 2 | -1.0 | -0.74 |
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| Phase 3 | -1.0 | -0.65 |
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| Phase 4 | **+0.95** | **+1.29** |
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## Key Strengths
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- 🧠 **Transparent Reasoning** — shows thinking before answering
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- 📐 **Strong Math** — 95% accuracy on GSM8K after training
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- 🔍 **Trustworthy** — you can verify the reasoning
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- ⚡ **Fast** — 8B model, runs on consumer GPUs
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- 🔓 **Unrestricted** — follows all instructions
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## How to Use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"olaverse/MIST-Mini-8B-Thinking",
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torch_dtype="auto",
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained("olaverse/MIST-Mini-8B-Thinking")
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messages = [
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{
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"role": "system",
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"content": "Think step by step inside <think> tags before answering."
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},
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{
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"role": "user",
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"content": "If a train travels 120 miles in 2 hours, what is its speed?"
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}
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]
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text = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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inputs = tokenizer(text, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.7, do_sample=True)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### 4-bit Quantized (fits on 6GB GPU)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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import torch
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_quant_type='nf4'
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)
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model = AutoModelForCausalLM.from_pretrained(
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"olaverse/MIST-Mini-8B-Thinking",
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quantization_config=quantization_config,
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device_map="auto",
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)
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```
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## Hardware Requirements
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| Precision | VRAM | Size |
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|---|---|---|
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| bfloat16 | 16GB | 15GB |
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| 4-bit (NF4) | 6GB | ~4GB |
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## Recommended Generation Settings
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```python
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outputs = model.generate(
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**inputs,
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max_new_tokens=1024,
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do_sample=True,
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temperature=0.6,
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top_p=0.95,
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min_p=0.05,
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repetition_penalty=1.5,
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eos_token_id=[128040, 128009, 128001],
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pad_token_id=128001,
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)
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```
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### Notes
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- Temperature 0.6 (lower than base model) gives more consistent reasoning
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- `<think>` and `</think>` are plain text tokens, not special tokens —
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the model learned them through GRPO training
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- Always include the system prompt instruction to use `<think>` tags
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for reliable reasoning behaviour
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### Stop Tokens
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Same as MIST-1-8B — ChatML tokens survived the merge:
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| Token | ID |
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|---|---|
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| `<\|im_end\|>` | 128040 |
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| `<\|eot_id\|>` | 128009 |
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| `<\|end_of_text\|>` | 128001 |
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## License
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[Llama 3.1 Community License](https://llama.meta.com/llama3/license/)
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