sangamw/finetunedLLamaModel7b

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Model Details

Model Description

This is a Llama-2-7b-chat-hf model fine-tuned using QLoRA on the mlabonne/guanaco-llama2-1k dataset. The goal of this fine-tuning was to adapt the base Llama-2 model for instruction following tasks, enhancing its ability to generate conversational and helpful responses.

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: Sangamw

• Model type: Causal Language Model, specifically a LlamaForCausalLM

• Language(s) (NLP): The language of the guanaco dataset (Spanish) and the base Llama-2 model (English, but fine-tuned for Spanish instructions).

• License: Llama 2 Community License

• Finetuned from model [optional]: meta-llama/Llama-2-7b-chat-hf

Model Sources [optional]

• Repository: [More Information Needed]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

This model can be used for various conversational AI tasks, instruction-following, and text generation where helpful and informative responses are desired. It has been fine-tuned on a dataset primarily in Spanish, making it suitable for Spanish-language applications

Downstream Use [optional]

Out-of-Scope Use

potential for bias, hallucinations, not suitable for critical applications without further safeguards

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForCausalLM from peft import PeftModel import torch

Load the base model with quantization if applicable (as you did during training)

For simplicity, here we load without quantization for inference, or you can include your bnb_config

base_model = AutoModelForCausalLM.from_pretrained( "meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16, # or torch.bfloat16 if your GPU supports it device_map="auto" )

Load the fine-tuned adapter

model = PeftModel.from_pretrained(base_model, "sangamw/finetunedLLamaModel7b") model = model.merge_and_unload() # Merge LoRA weights into the base model for easier use

Load the tokenizer

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")

Example inference

pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_new_tokens=200) result = pipe(f"[INST] Explain LoRA in simple terms.[/INST]") print(result[0]['generated_text'])

Training Details

Training Data

The model was fine-tuned on the mlabonne/guanaco-llama2-1k dataset, which consists of 1,000 instructions and responses, primarily in Spanish." You can link to the dataset card: https://huggingface.co/datasets/mlabonne/guanaco-llama2-1k

Training Procedure

Used QLoRA (4-bit quantization) with trl's SFTTrainer.

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

following are the TrainingArguments I have used ( num_train_epochs=1, per_device_train_batch_size=1, learning_rate=2e-4, optimizer="paged_adamw_32bit" ).

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

BibTeX:

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APA:

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Glossary [optional]

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Model Card Authors [optional]

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Model Card Contact

https://huggingface.co/sangamw/