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Safetensors
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README.md CHANGED
@@ -4,4 +4,81 @@ datasets:
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  - HuggingFaceH4/ultrachat_200k
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  base_model:
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  - HuggingFaceTB/SmolLM2-1.7B
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - HuggingFaceH4/ultrachat_200k
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  base_model:
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  - HuggingFaceTB/SmolLM2-1.7B
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+ library_name:
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+ - peft
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+ ---
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+
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+ # SmolLM2-1.7B-ultrachat_200k
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+
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+ Quantized Low Rank Adaptation (QLoRA) finetuned from HuggingFaceTB/SmolLM2-1.7B to UltraChat 200k dataset.
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+
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+ Model trained as an exercise in LLM post-training.
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+
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+ ## Model Details
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+
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+ - **Developed by:** Andrew Melbourne
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+ - **Model type:** Language Model
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+ - **License:** Apache 2.0
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+ - **Finetuned from model:** HuggingFaceTB/SmolLM2-1.7B
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+
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+ ### Model Sources
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+
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+ Training and inference scripts are available here.
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+ - **Repository:** [SmolLM2-1.7B-ultrachat_200k on Github](https://github.com/Melbourneandrew/SmolLM2-1.7B-Ultrachat_200k?tab=readme-ov-file)
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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+ ```python
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+ from peft import LoraConfig, get_peft_model, TaskType
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ model = AutoModelForCausalLM.from_pretrained("M3LBY/SmolLM2-1.7B-ultrachat_200k")
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+ tokenizer = AutoTokenizer.from_pretrained("M3LBY/SmolLM2-1.7B-ultrachat_200k")
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+
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+ messages = [{"role": "user", "content": "How far away is the sun?"}]
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+ prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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+ inputs = tokenizer(prompt, return_tensors="pt")
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+
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+ outputs = model.generate(**inputs)
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+ response = tokenizer.decode(outputs[0], skip_special_tokens=True)`
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+ print(response)
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+ ```
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+
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+ ## Training Details
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+
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+ The adapter model was trained using Supervised Fine-Tuning (SFT) with the following configuration:
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+
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+ - Base model: SmolLM2-1.7B
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+ - Mixed precision: bfloat16
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+ - Learning rate: 2e-5 with linear scheduler
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+ - Warmup ratio: 0.1
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+ - Training epochs: 1
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+ - Effective batch size: 32
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+ - Sequence length: 512 tokens
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+ - Flash Attention 2 enabled
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+
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+ Trained to a loss of 1.6965 after 6,496 steps.
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+
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+ Elapsed time: 2 hours 37 minutes.
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+
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+ Consumed ~22 Colab Compute Units for an estimated cost of $2.21 cents.
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+
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+ ## Evaluation
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+
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+
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+
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+ ## Citation [optional]
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+
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+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+
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+ **BibTeX:**
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+
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+ [More Information Needed]
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+
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+ **APA:**
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+
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+ [More Information Needed]
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+
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+ - PEFT 0.14.0%
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+ "peft_type": "LORA",
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+ "task_type": "CAUSAL_LM",
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+ "use_dora": false,
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+ "use_rslora": false
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+ }
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