Question Answering
Transformers
Safetensors
English
llama
text-generation
text-generation-inference
unsloth
ollama
mathematical-reasoning
Instructions to use NamrataThakur/llama31-8bn_SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NamrataThakur/llama31-8bn_SFT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="NamrataThakur/llama31-8bn_SFT")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NamrataThakur/llama31-8bn_SFT") model = AutoModelForCausalLM.from_pretrained("NamrataThakur/llama31-8bn_SFT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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| Base Model | Fine-Tuning | Train Dataset | Validation Loss | Evaluation Dataset | Mean Answer Relevancy Score | Mean Answer Correctness Score |
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| **Llama3.1-8bn** | Supervised Fine-Tuning | GSM8K | **1.12** | SmallThoughts | 0.736 | 0.437
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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| Base Model | Fine-Tuning | Train Dataset | Validation Loss | Evaluation Dataset | Mean Answer Relevancy Score | Mean Answer Correctness Score |
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| **Llama3.1-8bn** | Supervised Fine-Tuning | GSM8K | **1.12** | SmallThoughts | 0.736 | 0.437
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