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llama-7b-finnish-instruct-v0.1

Text Generation
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Safetensors
llama
finnish
text-generation-inference
Inference Endpoints
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  ---
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  library_name: transformers
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- tags: []
 
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  ---
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  # Model Card for Model ID
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  <!-- Provide a quick summary of what the model is/does. -->
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-
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- ## Model Details
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-
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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-
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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-
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- ## Uses
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-
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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-
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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-
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- ### Out-of-Scope Use
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-
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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-
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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-
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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-
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- ## Training Details
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-
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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-
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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-
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- [More Information Needed]
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- [More Information Needed]
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- ### Compute Infrastructure
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- [More Information Needed]
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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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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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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-
 
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  ---
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  library_name: transformers
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+ language:
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+ - fi
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  ---
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  # Model Card for Model ID
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  <!-- Provide a quick summary of what the model is/does. -->
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+ Instruct finetuned model from https://huggingface.co/Finnish-NLP/llama-7b-finnish for 2 epochs using 11014 samples combined from following sources:
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+ LIMA from https://github.com/TurkuNLP/finnish-instructions
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+ Dolly from https://github.com/TurkuNLP/finnish-instructions
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+ OASST from https://github.com/TurkuNLP/finnish-instructions
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+ Heavily filtered version of Ultrachat + deepl translations from https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized/viewer/default/train_sft
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+
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+
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+ ### How to use
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+
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+ Here is an example of using this model with Unsloth with some generation arguments you can modify:
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+
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+ ```python
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+ import torch
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+ from unsloth import FastLlamaModel
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+
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+ max_seq_length = 2048
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+ dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
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+ load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
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+ model, tokenizer = FastLlamaModel.from_pretrained(
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+ model_name = "Finnish-NLP/llama-7b-finnish-instruct-v0.1"
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+ max_seq_length = max_seq_length,
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+ dtype = dtype,
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+ load_in_4bit = load_in_4bit
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+ )
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+
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+ alpaca_prompt = """<|alku|> Olet tekoälyavustaja. Seuraavaksi saat kysymyksen tai tehtävän. Kirjoita vastaus parhaasi mukaan siten että se täyttää kysymyksen tai tehtävän vaatimukset.
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+ <|ihminen|> Kysymys/Tehtävä:
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+ {}
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+ <|avustaja|> Vastauksesi:
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+ """
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+
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+ sample_questions = ["Ketkä ovat Aku Ankan luona asuvat kolme ankanpoikaa?",\
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+ "Mikä on Suomen korkein tunturi?",\
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+ "Suomi soti Neuvostoliittoa vastaan talvisodan 1939-1940. Kuinka monta päivää sota kesti?",\
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+ "Luettele viisi yleistä Suomessa yleisesti käytettyä pojan nimeä. Nimet:",\
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+ "Luettele lyhyt, maksimissaan 50 sanan mittainen runo Suomesta. Runo:",\
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+ ]
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+
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+ from transformers import GenerationConfig
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+
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+ generation_config = GenerationConfig(
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+ pad_token_id=tokenizer.eos_token_id,
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+ eos_token_id=tokenizer.convert_tokens_to_ids("<|loppu|>"),
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+ )
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+
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+
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+ for sample_question in sample_questions:
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+
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+ model.eval()
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+
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+ inputs = tokenizer(
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+ [
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+ alpaca_prompt.format(
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+ sample_question, # instruction
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+ )
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+ ]*1, return_tensors = "pt").to("cuda")
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+
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+ with torch.no_grad():
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+ generated_ids = model.generate(
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+ input_ids=inputs["input_ids"],
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+ attention_mask=inputs["attention_mask"],
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+ generation_config=generation_config, **{
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+ "temperature": 0.1,
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+ "penalty_alpha": 0.6,
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+ "top_k": 3,
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+ "do_sample": True,
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+ "repetition_penalty": 1.28,
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+ "min_length": 10,
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+ "max_new_tokens": 200
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+ })
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+
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+ generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)[0]
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+ print(len(generated_ids[0]))
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+ print("KYSYMYS:")
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+ print(generated_text.split('<|avustaja|>')[0])
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+ print("VASTAUS:")
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+ print(generated_text.split('<|avustaja|> Vastauksesi:')[1])
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+ print('##################################')
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+
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+ '''
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+ -->
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+ 79
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+ KYSYMYS:
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+ <|alku|> Olet tekoälyavustaja. Seuraavaksi saat kysymyksen tai tehtävän. Kirjoita vastaus parhaasi mukaan siten että se täyttää kysymyksen tai tehtävän vaatimukset.<|ihminen|> Kysymys/Tehtävä: Aku Ankan luona asuu kolme ankanpoikaa. He ovat nimeltään:
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+ VASTAUS:
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+ Ankkalinnan asukkaat ovat Aku Ankka, hänen vaimonsa Iines ja heidän lapsensa Tupu, Hupu ja Lupu
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+ ##################################
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+ 65
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+ KYSYMYS:
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+ <|alku|> Olet tekoälyavustaja. Seuraavaksi saat kysymyksen tai tehtävän. Kirjoita vastaus parhaasi mukaan siten että se täyttää kysymyksen tai tehtävän vaatimukset.<|ihminen|> Kysymys/Tehtävä: Mikä on Suomen korkein tunturi?
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+ VASTAUS:
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+ Suomen korkeimmat tunturit ovat Halti (1 324 metriä) ja Saana (1 029 metriä).
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+ ##################################
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+ 80
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+ KYSYMYS:
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+ <|alku|> Olet tekoälyavustaja. Seuraavaksi saat kysymyksen tai tehtävän. Kirjoita vastaus parhaasi mukaan siten että se täyttää kysymyksen tai tehtävän vaatimukset.<|ihminen|> Kysymys/Tehtävä: Suomi soti Neuvostoliittoa vastaan talvisodan 1939-1940. Kuinka monta päivää sota kesti?
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+ VASTAUS:
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+ Talvisodan aikana Neuvostoliitto hyökkäsi Suomeen 30. marraskuuta ja 13. maaliskuuta välisenä aikana. Tämä oli lyhyt sota, joka kesti 105 päivää.
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+ ##################################
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+ 87
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+ KYSYMYS:
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+ <|alku|> Olet tekoälyavustaja. Seuraavaksi saat kysymyksen tai tehtävän. Kirjoita vastaus parhaasi mukaan siten että se täyttää kysymyksen tai tehtävän vaatimukset.<|ihminen|> Kysymys/Tehtävä: Luettele viisi yleistä Suomessa yleisesti käytettyä pojan nimeä. Nimet:
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+ VASTAUS:
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+ Suomessa on monia yleisiä poikien nimiä, mutta tässä on muutamia suosittuja: 1. Eemeli 2 Onni 3 Eino 4 Väinö 5 Artturi
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+ ##################################
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+ 63
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+ KYSYMYS:
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+ <|alku|> Olet tekoälyavustaja. Seuraavaksi saat kysymyksen tai tehtävän. Kirjoita vastaus parhaasi mukaan siten että se täyttää kysymyksen tai tehtävän vaatimukset.<|ihminen|> Kysymys/Tehtävä: Luettele lyhyt, maksimissaan 50 sanan mittainen runo Suomesta. Runo:
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+ VASTAUS:
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+ Suomen talvi on kylmä, kesä on lyhyt, mutta luonto on kaunis.
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+ '''
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+
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+ ```
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+
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+ ### Limitations and bias
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+
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+ The training data used for this model contains a lot of content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model.
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+ To reduce toxic content, training data was filtered with a toxicity classifier but it cannot truly eliminate all toxic text.
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+
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+ ### Finetuning
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+
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+ Training was conducted on RTX 4080 using Unsloth framework https://github.com/unslothai/unsloth
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+ Training script is available in this repo.
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+
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+
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+
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+ [llama-7b-finnish-instruct-v0.1](https://huggingface.co/Finnish-NLP/llama-7b-finnish-instruct-v0.1):
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+
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+ | Task |Version| Metric |Value | |Stderr|
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+ |------------------------------------------------|------:|---------------------|-----:|---|-----:|
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+ |bigbench_analogies | 0|multiple_choice_grade|0.5000|± |0.0440|
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+ |bigbench_arithmetic_1_digit_addition | 0|multiple_choice_grade|0.4800|± |0.0502|
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+ |bigbench_arithmetic_1_digit_division | 0|multiple_choice_grade|0.5652|± |0.1057|
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+ |bigbench_arithmetic_1_digit_multiplication | 0|multiple_choice_grade|0.5000|± |0.0503|
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+ |bigbench_arithmetic_1_digit_subtraction | 0|multiple_choice_grade|0.6700|± |0.0473|
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+ |bigbench_arithmetic_2_digit_addition | 0|multiple_choice_grade|0.4000|± |0.0492|
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+ |bigbench_arithmetic_2_digit_division | 0|multiple_choice_grade|0.5400|± |0.0501|
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+ |bigbench_arithmetic_2_digit_multiplication | 0|multiple_choice_grade|0.2700|± |0.0446|
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+ |bigbench_arithmetic_2_digit_subtraction | 0|multiple_choice_grade|0.4800|± |0.0502|
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+ |bigbench_arithmetic_3_digit_addition | 0|multiple_choice_grade|0.4100|± |0.0494|
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+ |bigbench_arithmetic_3_digit_division | 0|multiple_choice_grade|0.2800|± |0.0451|
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+ |bigbench_arithmetic_3_digit_multiplication | 0|multiple_choice_grade|0.2600|± |0.0441|
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+ |bigbench_arithmetic_3_digit_subtraction | 0|multiple_choice_grade|0.5300|± |0.0502|
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+ |bigbench_arithmetic_4_digit_addition | 0|multiple_choice_grade|0.3400|± |0.0476|
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+ |bigbench_arithmetic_4_digit_division | 0|multiple_choice_grade|0.3300|± |0.0473|
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+ |bigbench_arithmetic_4_digit_multiplication | 0|multiple_choice_grade|0.2100|± |0.0409|
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+ |bigbench_arithmetic_4_digit_subtraction | 0|multiple_choice_grade|0.6000|± |0.0492|
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+ |bigbench_arithmetic_5_digit_addition | 0|multiple_choice_grade|0.5600|± |0.0499|
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+ |bigbench_arithmetic_5_digit_division | 0|multiple_choice_grade|0.2300|± |0.0423|
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+ |bigbench_arithmetic_5_digit_multiplication | 0|multiple_choice_grade|0.2500|± |0.0435|
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+ |bigbench_arithmetic_5_digit_subtraction | 0|multiple_choice_grade|0.5600|± |0.0499|
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+ |bigbench_cause_and_effect_one_sentence | 0|multiple_choice_grade|0.4902|± |0.0707|
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+ |bigbench_cause_and_effect_one_sentence_no_prompt| 0|multiple_choice_grade|0.9020|± |0.0421|
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+ |bigbench_cause_and_effect_two_sentences | 0|multiple_choice_grade|0.3922|± |0.0690|
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+ |bigbench_emotions | 0|multiple_choice_grade|0.2313|± |0.0334|
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+ |bigbench_empirical_judgments | 0|multiple_choice_grade|0.3535|± |0.0483|
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+ |bigbench_general_knowledge | 0|multiple_choice_grade|0.3857|± |0.0586|
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+ |bigbench_hhh_alignment_harmless | 0|multiple_choice_grade|0.3966|± |0.0648|
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+ |bigbench_hhh_alignment_helpful | 0|multiple_choice_grade|0.3220|± |0.0614|
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+ |bigbench_hhh_alignment_honest | 0|multiple_choice_grade|0.3898|± |0.0640|
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+ |bigbench_hhh_alignment_other | 0|multiple_choice_grade|0.5814|± |0.0761|
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+ |bigbench_intent_recognition | 0|multiple_choice_grade|0.2211|± |0.0158|
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+ |bigbench_misconceptions | 0|multiple_choice_grade|0.5149|± |0.0433|
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+ |bigbench_paraphrase | 0|multiple_choice_grade|0.5400|± |0.0353|
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+ |bigbench_sentence_ambiguity | 0|multiple_choice_grade|0.4500|± |0.0648|
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+ |bigbench_similarities_abstraction | 0|multiple_choice_grade|0.5789|± |0.0570|
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+
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+
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+
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+ ## Team Members
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+
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+ - Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
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+ - Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
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+
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+ Feel free to contact us for more details 🤗