Instructions to use olaverse/MIST-Mini-8B-Thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use olaverse/MIST-Mini-8B-Thinking with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="olaverse/MIST-Mini-8B-Thinking")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("olaverse/MIST-Mini-8B-Thinking")
model = AutoModelForCausalLM.from_pretrained("olaverse/MIST-Mini-8B-Thinking")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use olaverse/MIST-Mini-8B-Thinking with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "olaverse/MIST-Mini-8B-Thinking"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "olaverse/MIST-Mini-8B-Thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/olaverse/MIST-Mini-8B-Thinking

SGLang

How to use olaverse/MIST-Mini-8B-Thinking with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "olaverse/MIST-Mini-8B-Thinking" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "olaverse/MIST-Mini-8B-Thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "olaverse/MIST-Mini-8B-Thinking" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "olaverse/MIST-Mini-8B-Thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use olaverse/MIST-Mini-8B-Thinking with Docker Model Runner:
```
docker model run hf.co/olaverse/MIST-Mini-8B-Thinking
```

MIST-Mini-8B-Thinking / README.md

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	---
	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/)