Transformers
Safetensors
Generated from Trainer
rl-swarm
grpo
gensyn
I am soft pensive ant
unsloth
trl
Instructions to use Tiba/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-soft_pensive_ant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tiba/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-soft_pensive_ant with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Tiba/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-soft_pensive_ant", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Tiba/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-soft_pensive_ant 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 Tiba/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-soft_pensive_ant 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 Tiba/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-soft_pensive_ant to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Tiba/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-soft_pensive_ant to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Tiba/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-soft_pensive_ant", max_seq_length=2048, )
End of training
Browse files- adapter_model.safetensors +1 -1
- all_results.json +4 -4
- train_results.json +4 -4
- trainer_state.json +0 -0
adapter_model.safetensors
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all_results.json
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{
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"train_loss": 4.248581079082214e-05,
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"train_runtime": 2113.9117,
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"train_samples": 38,
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"train_samples_per_second": 1.514,
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"train_steps_per_second": 0.095
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train_results.json
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"train_loss":
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"train_runtime":
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"train_samples": 38,
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"train_samples_per_second": 1.
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"train_steps_per_second": 0.
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{
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"total_flos": 0.0,
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"train_loss": 4.248581079082214e-05,
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"train_runtime": 2113.9117,
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"train_samples": 38,
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"train_samples_per_second": 1.514,
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"train_steps_per_second": 0.095
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trainer_state.json
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