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README.md

@@ -1,119 +1,202 @@
----
-base_model: meta-llama/Llama-3.1-8B-Instruct
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- llama
-- trl
-license: apache-2.0
-language:
-- lb
-- en
----
-# Lux-Llama
-
-This repository contains a fine-tuned version of the Llama-3.1-8B-Instruct model, specifically adapted for Luxembourgish. The fine-tuning was performed using LoRA (Low-Rank Adaptation) on a dataset crafted to generate Chain-of-Thought (CoT) reasoning in Luxembourgish. The fine-tuning process utilized the computational resources provided by [Meluxina](https://www.luxprovide.lu/meluxina), a high-performance computing (HPC) platform operated by LuxProvide.
-
-## Model Overview
-- **Base Model:** Llama-3.1-8B-Instruct
-- **Fine-Tuning Method:** LoRA (Low-Rank Adaptation)
-- **Dataset:** Luxembourgish Chain-of-Thought (CoT) dataset
-- **Compute Platform:** Meluxina by LuxProvide
-- **Fine-Tuning Framework:** [Unsloth](https://github.com/unsloth/unsloth)
-
-## About Meluxina
-[Meluxina](https://www.luxprovide.lu/meluxina) is Luxembourg's national supercomputer, launched in June 2021 by LuxProvide. It is built on the EVIDEN BullSequana XH2000 platform and provides:
-- **18 PetaFlops** of computing power.
-- **20 PetaBytes** of storage capacity.
-- A **scalable architecture** integrating simulation, modeling, data analytics, and AI.
-
-Meluxina was ranked 36th globally and recognized as the greenest supercomputer in the EU within the Top500 ranking. Named after Luxembourg's legend of the mermaid Melusina, it symbolizes digital innovation and employs water-cooling technology for energy efficiency.
-
-## Features
-- **Language:** Luxembourgish
-- **Specialization:** Chain-of-Thought reasoning for complex problem-solving and step-by-step explanations.
-- **Efficiency:** LoRA fine-tuning ensures minimal computational overhead while maintaining high performance.
-
-## Installation
-To use the fine-tuned model, ensure you have the following dependencies installed:
-
-```python
-%%capture
-!pip install unsloth
-# Also get the latest nightly Unsloth!
-!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git
-```
-
-You can then load the model as follows:
-
-```python
-from unsloth import FastLanguageModel
-import torch
-max_seq_length = 8192 # Choose any! We auto support RoPE Scaling internally!
-dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
-load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
-
-model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name = "aiplanet/Lux-Llama",
-    max_seq_length = max_seq_length,
-    dtype = dtype,
-    load_in_4bit = load_in_4bit,
-)
-FastLanguageModel.for_inference(model) # Enable native 2x faster inference
-
-alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
-
-### Instruction:
-{}
-
-### Input:
-{}
-
-### Response:
-{}"""
-
-inputs = tokenizer(
-[
-    alpaca_prompt.format(
-        "You are a helpful chatbot. Your primary task is to provide clear and relevant responses to user queries. Always respond in Luxembourgish unless the user explicitly requests otherwise. You must NOT provide a translation for your reply.", # instruction
-        "Erklär mech Schrëtt fir Schrëtt, wéi ee Brout bäckt.", # input
-        "", # output - leave this blank for generation!
-    )
-], return_tensors = "pt").to("cuda")
-
-outputs = model.generate(**inputs, max_new_tokens = 512, repetition_penalty = 1.2)
-# tokenizer.batch_decode(outputs, skip_special_tokens = True
-tokenizer.batch_decode(outputs)
-```
-
-Output:
-
-```python
-# Fir e purescht Brout ze baaken, brauchst du een Härzchen (ca 250g) an d'Zucker (50g). D'Recept kann och mat Käsekugeln oder Nüssen ergänzt ginn. Et ass wichteg, dass ee gutt nachbaket! Ee muss also den Ofen op 180°C schalten an de Brot am ëmmersten drënne Plätz vun der Ablage stellen. De Baackezäit hängt vu der Gréisst vum Bierg an dem Wénkel, dee ee beim Bauen benutzt. Fir en normale Brotpartee staavwouert et zirka 45-60 Minutten. Määnter net, ob ee dat selwer ausprobiieren wollet!
-```
-
-## Dataset Details
-The Luxembourgish Chain-of-Thought (CoT) dataset used for fine-tuning consists of:
-- **Examples:** Handcrafted and curated examples of multi-step reasoning tasks.
-- **Domains:** Mathematics, general knowledge, and real-life problem-solving scenarios.
-- **Format:** Text sequences with labeled CoT reasoning in Luxembourgish.
-
-## Fine-Tuning Process
-1. **Framework:** The fine-tuning was conducted using [Unsloth](https://github.com/unsloth/unsloth), a LoRA-based fine-tuning library.
-2. **Steps:**
-   - Initialized the Llama-3.1-8B-Instruct model.
-   - Applied LoRA adapters for efficient training.
-   - Fine-tuned using the Luxembourgish CoT dataset on the Meluxina HPC cluster.
-3. **Hardware:** High-performance A100 GPUs provided by Meluxina ensured rapid convergence.
-
-## Benchmarking
-- *Under progress*
-
-## Acknowledgments
-This work leverages computational resources and support from [Meluxina](https://www.luxprovide.lu/meluxina) by LuxProvide.
-  
-<img src="https://www.luxprovide.lu/wp-content/themes/luxprovide2023/public/images/logo/logo_notagline_color_blue.4b07cb.svg" alt="LuxProvide Logo" width="50%">
-
-<img src="https://docs.lxp.lu/FAQ/images/MeluXina_Logo.png" alt="Meluxina Logo" width="50%">
-
+---
+base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+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.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- 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. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+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).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.13.2

adapter_config.json

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-{
-  "alpha_pattern": {},
-  "auto_mapping": null,
-  "base_model_name_or_path": "unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
-  "bias": "none",
-  "fan_in_fan_out": false,
-  "inference_mode": true,
-  "init_lora_weights": true,
-  "layer_replication": null,
-  "layers_pattern": null,
-  "layers_to_transform": null,
-  "loftq_config": {},
-  "lora_alpha": 16,
-  "lora_dropout": 0,
-  "megatron_config": null,
-  "megatron_core": "megatron.core",
-  "modules_to_save": null,
-  "peft_type": "LORA",
-  "r": 16,
-  "rank_pattern": {},
-  "revision": null,
-  "target_modules": [
-    "o_proj",
-    "v_proj",
-    "gate_proj",
-    "q_proj",
-    "k_proj",
-    "up_proj",
-    "down_proj"
-  ],
-  "task_type": "CAUSAL_LM",
-  "use_dora": false,
-  "use_rslora": false
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_dropout": 0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "o_proj",
+    "v_proj",
+    "up_proj",
+    "k_proj",
+    "gate_proj",
+    "q_proj",
+    "down_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
 }

adapter_model.safetensors

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 version https://git-lfs.github.com/spec/v1
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-size 167832240
+oid sha256:973f11c37751e93450ba308b7deedee93975d4287d44e0037b9f82630298de36
+size 83945296

special_tokens_map.json

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-{
-  "bos_token": {
-    "content": "<|begin_of_text|>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "eos_token": {
-    "content": "<|eot_id|>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "pad_token": {
-    "content": "<|finetune_right_pad_id|>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  }
-}
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|finetune_right_pad_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}