Instructions to use msfm/llm-jp-3-13b-ichikara_all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use msfm/llm-jp-3-13b-ichikara_all with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("msfm/llm-jp-3-13b-ichikara_all", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use msfm/llm-jp-3-13b-ichikara_all with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for msfm/llm-jp-3-13b-ichikara_all to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for msfm/llm-jp-3-13b-ichikara_all to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for msfm/llm-jp-3-13b-ichikara_all to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="msfm/llm-jp-3-13b-ichikara_all", max_seq_length=2048, )
Uploaded model
- Developed by: msfm
- License: apache-2.0
- Finetuned from model : llm-jp/llm-jp-3-13b
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
Example
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="msfm/llm-jp-3-13b-ichikara_all",
dtype=dtype,
load_in_4bit=True,
trust_remote_code=True,
)
FastLanguageModel.for_inference(model)
input = "้็้ธๆใไปใทใผใบใณๆดป่บใใใใใซๅใ็ตใในใ5ใคใฎใใจใๆใใฆใใ ใใใ"
prompt = f"""### ๆ็คบ\n{input}\n### ๅ็ญ\n"""
inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens = 2048, use_cache = True, do_sample=False, repetition_penalty=1.2)
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### ๅ็ญ')[-1]
Inference Providers NEW
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Base model
llm-jp/llm-jp-3-13b