Instructions to use Corianas/Microllama_Char_88k_step with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Corianas/Microllama_Char_88k_step with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Corianas/Microllama_Char_88k_step")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Corianas/Microllama_Char_88k_step") model = AutoModelForCausalLM.from_pretrained("Corianas/Microllama_Char_88k_step") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Corianas/Microllama_Char_88k_step with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Corianas/Microllama_Char_88k_step" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Corianas/Microllama_Char_88k_step", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Corianas/Microllama_Char_88k_step
- SGLang
How to use Corianas/Microllama_Char_88k_step 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 "Corianas/Microllama_Char_88k_step" \ --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": "Corianas/Microllama_Char_88k_step", "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 "Corianas/Microllama_Char_88k_step" \ --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": "Corianas/Microllama_Char_88k_step", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Corianas/Microllama_Char_88k_step with Docker Model Runner:
docker model run hf.co/Corianas/Microllama_Char_88k_step
This is a character (english a-z 0-9 and so on) trained model following Andrej karpathy's llama.c project https://github.com/karpathy/llama2.c on both TinyStories and my own internal similar dataset I made.
for it to see/output Uppercase letters this model uses a Shift-Key modifier before the letter to become uppercase, and has never been trained on actual uppercase letters.
This modifier is ↨ and here are the functions I use to convert from straight text to the modified format and back.
def add_caseifer(text):
# Using list comprehension for more efficient concatenation
return ''.join(['↨' + char.lower() if char.isupper() else char for char in text
def remove_caseifer(text):
new_text = ""
i = 0
while i < len(text):
if text[i] == "↨":
if i+1 < len(text):
new_text += text[i+1].upper()
i += 1
else:
pass # skip this index
else:
new_text += text[i]
i += 1
return new_text
As such for test strings to use in chat try using somthing like:
↨hello, my name is ↨clara and ↨i like
This model was only uploaded as a test to see if I got it all HF compatible, and was able to use toold like LazyMergekit on it and yes, it did work. happydance
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