Instructions to use AxionLab-official/MiniBot-0.9M-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AxionLab-official/MiniBot-0.9M-Base with Transformers:

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

pipe = pipeline("text-generation", model="AxionLab-official/MiniBot-0.9M-Base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AxionLab-official/MiniBot-0.9M-Base")
model = AutoModelForCausalLM.from_pretrained("AxionLab-official/MiniBot-0.9M-Base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AxionLab-official/MiniBot-0.9M-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AxionLab-official/MiniBot-0.9M-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AxionLab-official/MiniBot-0.9M-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AxionLab-official/MiniBot-0.9M-Base

SGLang

How to use AxionLab-official/MiniBot-0.9M-Base 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 "AxionLab-official/MiniBot-0.9M-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AxionLab-official/MiniBot-0.9M-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "AxionLab-official/MiniBot-0.9M-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AxionLab-official/MiniBot-0.9M-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AxionLab-official/MiniBot-0.9M-Base with Docker Model Runner:
```
docker model run hf.co/AxionLab-official/MiniBot-0.9M-Base
```

AxionLab-official commited on Apr 5

Commit

a355081

verified ·

1 Parent(s): 75b02a4

Update README.md

Browse files

Files changed (1) hide show

README.md +10 -9

README.md CHANGED Viewed

@@ -51,16 +51,15 @@ Dataset
 The model was trained on a Portuguese conversational dataset, including:
-Dialogues (Usuário ↔ Bot)
-Perguntas e respostas simples
-Conversas casuais
-Estruturas de linguagem natural
-Format
-User: Oi!
-Bot: Olá! Como posso te ajudar?
 Training Notes
 Focused on language pattern learning, not reasoning
 No instruction tuning (no RLHF, no alignment)
 Lightweight training pipeline
 Optimized for small-scale experimentation
 💡 Capabilities
@@ -91,7 +90,7 @@ model_name = "AxionLab-official/MiniBot-0.9M-Base"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
-prompt = "User: Oi\nBot:"
 inputs = tokenizer(prompt, return_tensors="pt")
 outputs = model.generate(
@@ -101,8 +100,10 @@ outputs = model.generate(
     top_p=0.95,
     do_sample=True
 )
-```
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ⚙️ Recommended Generation Settings
 For better results:

 The model was trained on a Portuguese conversational dataset, including:
+Pure text
 Training Notes
 Focused on language pattern learning, not reasoning
 No instruction tuning (no RLHF, no alignment)
 Lightweight training pipeline
 Optimized for small-scale experimentation
 💡 Capabilities
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
+prompt = "The cat "
 inputs = tokenizer(prompt, return_tensors="pt")
 outputs = model.generate(
     top_p=0.95,
     do_sample=True
 )
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 ⚙️ Recommended Generation Settings
 For better results: