Text Generation
PEFT
Safetensors
English
code
cobol
java
code-translation
mainframe-modernization
lora
sft
trl
unsloth
ministral
conversational
Eval Results (legacy)
Instructions to use axeltta/mistral-axel-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use axeltta/mistral-axel-2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/ministral-3-8b-instruct-2512-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "axeltta/mistral-axel-2") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use axeltta/mistral-axel-2 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 axeltta/mistral-axel-2 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 axeltta/mistral-axel-2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for axeltta/mistral-axel-2 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="axeltta/mistral-axel-2", max_seq_length=2048, )
- Xet hash:
- 6c1f409394a7e7f06f3ec706ec85360557b3392bf90b38e38ee23d6535a6cdc3
- Size of remote file:
- 107 MB
- SHA256:
- a294625cc8c3e7e157e98b5becece3ed1f4a22002bc42a761d1d8260ae3f11b2
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