Text Generation
PEFT
TensorBoard
Safetensors
English
unsloth
lora
qlora
vulnerability-detection
security
code-analysis
cybersecurity
ultival
adapter
conversational
Instructions to use starsofchance/Mistral-Unsloth-QLoRA-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use starsofchance/Mistral-Unsloth-QLoRA-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Ministral-8B-Instruct-2410") model = PeftModel.from_pretrained(base_model, "starsofchance/Mistral-Unsloth-QLoRA-adapter") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use starsofchance/Mistral-Unsloth-QLoRA-adapter 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 starsofchance/Mistral-Unsloth-QLoRA-adapter 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 starsofchance/Mistral-Unsloth-QLoRA-adapter to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for starsofchance/Mistral-Unsloth-QLoRA-adapter to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="starsofchance/Mistral-Unsloth-QLoRA-adapter", max_seq_length=2048, )
Upload model trained with Unsloth
Browse filesUpload model trained with Unsloth 2x faster
- .gitattributes +1 -0
- chat_template.jinja +1 -0
- special_tokens_map.json +30 -0
- tokenizer.json +3 -0
- tokenizer_config.json +0 -0
.gitattributes
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@@ -37,3 +37,4 @@ ultival_mistral/checkpoint-4000/tokenizer.json filter=lfs diff=lfs merge=lfs -te
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ultival_mistral/checkpoint-5000/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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ultival_mistral/checkpoint-6000/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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ultival_mistral/checkpoint-6184/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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ultival_mistral/checkpoint-5000/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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ultival_mistral/checkpoint-6000/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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ultival_mistral/checkpoint-6184/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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chat_template.jinja
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{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% if messages[1]['role'] == 'user' %}{{ '[INST] ' + messages[0]['content'] + ' ' + messages[1]['content'] + ' [/INST]' }}{% set loop_messages = messages[2:] %}{% else %}{{ '[INST] ' + messages[0]['content'] + ' [/INST]' }}{% set loop_messages = messages[1:] %}{% endif %}{% else %}{% set loop_messages = messages %}{% endif %}{% for message in loop_messages %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<pad>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:be2965d3e435dbdef9376e5bdb3dcc2bfc7e271aa5152ae896cf53c7e920adf8
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size 17078235
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tokenizer_config.json
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