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Model: barbaroo/gptsw3-6.7B-translation-en-fo
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: AI-Sweden-Models/gpt-sw3-6.7b-v2
+library_name: transformers
+datasets:
+- barbaroo/Sprotin_parallel
+- barbaroo/fo_en_synthetic
+language:
+- en
+- fo
+metrics:
+- bleu
+- chrf
+- bertscore
+pipeline_tag: text-generation
+---
+
+# Model Card: English–Faroese Translation (Merged Model)
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Barbara Scalvini  
+- **Model type:** Fully merged model for **English → Faroese** translation  
+- **Languages:** English, Faroese  
+- **License:** Inherits license from the base model (GPT-SW3 6.7B)  
+- **Finetuned from:** [AI-Sweden-Models/gpt-sw3-6.7b-v2](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2)  
+- **Library:** [Transformers](https://github.com/huggingface/transformers)  
+
+This model is the **merged version** of the PEFT adapter [`barbaroo/gptsw3_translate_synth_6.7B`](https://huggingface.co/barbaroo/gptsw3_translate_synth_6.7B) with its base model.  
+
+---
+
+## Uses
+
+### Direct Use
+- English → Faroese machine translation.  
+
+### Downstream Use
+- Can be integrated into **multilingual NLP pipelines** or localization workflows.  
+
+### Out-of-Scope Use
+- Languages other than English or Faroese.  
+- Tasks like summarization, classification, or dialogue without further fine-tuning.  
+
+---
+
+## Bias, Risks, and Limitations
+- As with all translation models, may reflect **biases** from the training corpora.  
+- Outputs should be **carefully validated** for sensitive or high-stakes domains.  
+
+---
+
+## How to Get Started with the Model
+
+```python
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import re
+import pandas as pd
+
+# Model repo
+MODEL_NAME = "barbaroo/gptsw3-6.7B-translation-en-fo"
+
+# Quantization config (8-bit)
+bnb_config = BitsAndBytesConfig(
+    load_in_8bit=True
+)
+
+# Initialize tokenizer & model
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    quantization_config=bnb_config,
+    device_map="auto",
+)
+model.eval()
+
+# Alpaca-style prompt template
+alpaca_prompt = """
+
+### Instruction:
+{}
+
+### Input:
+{}
+
+### Response:
+{}"""
+
+EOS_TOKEN = tokenizer.eos_token
+print("EOS token:", EOS_TOKEN)
+
+# Example sentences
+sentences = ["I love Faroese!"]
+translations = []
+
+for sentence in sentences:
+    inputs = tokenizer(
+        [
+            alpaca_prompt.format(
+                "Translate this sentence from English to Faroese:",
+                sentence,
+                "",
+            )
+        ],
+        return_tensors="pt"
+    ).to("cuda")
+
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=500,
+        use_cache=True,
+        do_sample=True,
+        temperature=0.1,
+        top_p=1,
+    )
+
+    output_string = tokenizer.batch_decode(outputs, skip_special_tokens=False)[0]
+
+    try:
+        response = output_string.split("Response:\n", 1)[1]
+        translation = response.replace(EOS_TOKEN, "")
+    except IndexError:
+        translation = ""
+
+    translations.append(translation)
+    print(translation)
+
+```
+
+
+
+
+
+
+
+
+
+## Training Details
+
+### Training Data
+- [barbaroo/Sprotin_parallel](https://huggingface.co/datasets/barbaroo/Sprotin_parallel)  
+- [barbaroo/fo_en_synthetic](https://huggingface.co/datasets/barbaroo/fo_en_synthetic)  
+
+### Procedure
+- Initially trained as a **PEFT adapter** using Alpaca-style prompts.  
+- Then **merged with the base GPT-SW3 6.7B model** to produce this standalone version.  
+
+**Hyperparameters:**  
+- Epochs: 3 (early stopping on validation loss)  
+- Batch Size: 2 (with 4 gradient accumulation steps)  
+- Learning Rate: 2e-4  
+- Optimizer: AdamW with LR scheduler + warm-up  
+
+---
+
+## Evaluation
+
+### Test Data
+- FLORES-200 benchmark (~1012 English–Faroese pairs).  
+
+### Metrics
+- **BLEU:** 19.8  
+- **chrF:** 52.4
+

config.json

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+{
+  "activation_function": "gelu",
+  "apply_query_key_layer_scaling": true,
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 1,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 3,
+  "initializer_range": 0.01,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 2048,
+  "n_embd": 4096,
+  "n_head": 32,
+  "n_inner": 16384,
+  "n_layer": 32,
+  "n_positions": 2048,
+  "normalize_attention_scores": true,
+  "pad_token_id": 0,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "tokenizer_class": "GPTSw3Tokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.50.3",
+  "use_cache": true,
+  "vocab_size": 64000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 3,
+  "pad_token_id": 0,
+  "transformers_version": "4.50.3"
+}

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special_tokens_map.json

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spiece.model

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tokenizer_config.json

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