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 Settings

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
```

olumideola commited on 2 days ago

Commit

7f806db

verified ·

1 Parent(s): 83aabe7

Update README.md

Browse files

Files changed (1) hide show

README.md +32 -0

README.md CHANGED Viewed

@@ -129,6 +129,38 @@ model = AutoModelForCausalLM.from_pretrained(
 | bfloat16 | 16GB | 15GB |
 | 4-bit (NF4) | 6GB | ~4GB |
 ## License
 [Llama 3.1 Community License](https://llama.meta.com/llama3/license/)

 | 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/)