jpacifico
/

Chocolatine-14B-Instruct-DPO-v1.2

@@ -1,199 +1,150 @@
 ---
 library_name: transformers
-tags: []
 ---
-# 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. -->
-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:** [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]

 ---
 library_name: transformers
+license: mit
+language:
+- fr
+- en
+tags:
+- french
+- chocolatine
+datasets:
+- jpacifico/french-orca-dpo-pairs-revised
+pipeline_tag: text-generation
 ---
+### Chocolatine-14B-Instruct-DPO-v1.2
+DPO fine-tuned of [microsoft/Phi-3-medium-4k-instruct](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) (14B params)
+using the [jpacifico/french-orca-dpo-pairs-revised](https://huggingface.co/datasets/jpacifico/french-orca-dpo-pairs-revised) rlhf dataset.
+Training in French also improves the model in English, surpassing the performances of its base model.
+Window context = 4k tokens
+**Note: Version 1.2 is an improvement in French compared to the previous version.
+For English usage, however, [version 1.1](https://huggingface.co/jpacifico/Chocolatine-14B-Instruct-DPO-v1.1) is recommended as it is more robust in English.**
+### MT-Bench-French
+Chocolatine-14B-Instruct-DPO-v1.2 is outperforming Phi-3-medium-4k-instruct and its previous version in French tasks.
+Please note that the [Chocolatine-3B](jpacifico/Chocolatine-3B-Instruct-DPO-Revised) model is very close to Phi-3-Medium in French, which is a significant achievement.
+```
+########## First turn ##########
+                                                      score
+model                                         turn
+gpt-4o-mini                                   1     9.28750
+Chocolatine-14B-Instruct-4k-DPO               1     8.63750
+Chocolatine-14B-Instruct-DPO-v1.2             1     8.61250
+Phi-3-medium-4k-instruct                      1     8.22500
+gpt-3.5-turbo                                 1     8.13750
+Chocolatine-3B-Instruct-DPO-Revised           1     7.98750
+Daredevil-8B                                  1     7.88750
+Daredevil-8B-abliterated                      1     7.83750
+Chocolatine-3B-Instruct-DPO-v1.0              1     7.68750
+NeuralDaredevil-8B-abliterated                1     7.62500
+Phi-3-mini-4k-instruct                        1     7.21250
+Meta-Llama-3-8B-Instruct                      1     7.16250
+Meta-Llama-3.1-8B-Instruct                    1     7.05000
+vigostral-7b-chat                             1     6.78750
+Mistral-7B-Instruct-v0.3                      1     6.75000
+gemma-2-2b-it                                 1     6.45000
+Mistral-7B-Instruct-v0.2                      1     6.28750
+French-Alpaca-7B-Instruct_beta                1     5.68750
+vigogne-2-7b-chat                             1     5.66250
+vigogne-2-7b-instruct                         1     5.13750
+########## Second turn ##########
+                                                       score
+model                                         turn
+gpt-4o-mini                                   2     8.912500
+Chocolatine-14B-Instruct-DPO-v1.2             2     8.337500
+Chocolatine-3B-Instruct-DPO-Revised           2     7.937500
+Phi-3-medium-4k-instruct                      2     7.750000
+Chocolatine-14B-Instruct-4k-DPO               2     7.737500
+gpt-3.5-turbo                                 2     7.679167
+Chocolatine-3B-Instruct-DPO-v1.0              2     7.612500
+NeuralDaredevil-8B-abliterated                2     7.125000
+Daredevil-8B                                  2     7.087500
+Daredevil-8B-abliterated                      2     6.873418
+Meta-Llama-3-8B-Instruct                      2     6.800000
+Meta-Llama-3.1-8B-Instruct                    2     6.787500
+Mistral-7B-Instruct-v0.2                      2     6.512500
+Mistral-7B-Instruct-v0.3                      2     6.500000
+Phi-3-mini-4k-instruct                        2     6.487500
+vigostral-7b-chat                             2     6.162500
+gemma-2-2b-it                                 2     6.100000
+French-Alpaca-7B-Instruct_beta                2     5.487395
+vigogne-2-7b-chat                             2     2.775000
+vigogne-2-7b-instruct                         2     2.240506
+########## Average ##########
+                                                  score
+model
+gpt-4o-mini                                    9.100000
+Chocolatine-14B-Instruct-DPO-v1.2              8.475000
+Chocolatine-14B-Instruct-4k-DPO                8.187500
+Phi-3-medium-4k-instruct                       7.987500
+Chocolatine-3B-Instruct-DPO-Revised            7.962500
+gpt-3.5-turbo                                  7.908333
+Chocolatine-3B-Instruct-DPO-v1.0               7.650000
+Daredevil-8B                                   7.487500
+NeuralDaredevil-8B-abliterated                 7.375000
+Daredevil-8B-abliterated                       7.358491
+Meta-Llama-3-8B-Instruct                       6.981250
+Meta-Llama-3.1-8B-Instruct                     6.918750
+Phi-3-mini-4k-instruct                         6.850000
+Mistral-7B-Instruct-v0.3                       6.625000
+vigostral-7b-chat                              6.475000
+Mistral-7B-Instruct-v0.2                       6.400000
+gemma-2-2b-it                                  6.275000
+French-Alpaca-7B-Instruct_beta                 5.587866
+vigogne-2-7b-chat                              4.218750
+vigogne-2-7b-instruct                          3.698113
+```
+### Usage
+You can run this model using my [Colab notebook](https://github.com/jpacifico/Chocolatine-LLM/blob/main/Chocolatine_14B_inference_test_colab.ipynb)
+You can also run Chocolatine using the following code:
+```python
+import transformers
+from transformers import AutoTokenizer
+# Format prompt
+message = [
+    {"role": "system", "content": "You are a helpful assistant chatbot."},
+    {"role": "user", "content": "What is a Large Language Model?"}
+]
+tokenizer = AutoTokenizer.from_pretrained(new_model)
+prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
+# Create pipeline
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=new_model,
+    tokenizer=tokenizer
+)
+# Generate text
+sequences = pipeline(
+    prompt,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+    num_return_sequences=1,
+    max_length=200,
+)
+print(sequences[0]['generated_text'])
+```
+### Limitations
+The Chocolatine model is a quick demonstration that a base model can be easily fine-tuned to achieve compelling performance.
+It does not have any moderation mechanism.
+- **Developed by:** Jonathan Pacifico, 2024
+- **Model type:** LLM
+- **Language(s) (NLP):** French, English
+- **License:** MIT