jpacifico
/

Chocolatine-14B-Instruct-4k-DPO

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 ---
 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-4k-DPO
+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
+### Benchmarks
+Submitted on the [OpenLLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) (aug 2024)
+Results coming soon.
+### MT-Bench-French
+Chocolatine-14B-Instruct-4k-DPO is outperforming GPT-3.5-Turbo and Phi-3-medium-4k-instruct on [MT-Bench-French](https://huggingface.co/datasets/bofenghuang/mt-bench-french),
+used with [multilingual-mt-bench](https://github.com/Peter-Devine/multilingual_mt_bench)
+```
+########## First turn ##########
+                                           score
+model                               turn
+Chocolatine-14B-Instruct-4k-DPO     1     8.6375
+Phi-3-medium-4k-instruct            1     8.2250
+gpt-3.5-turbo                       1     8.1375
+Chocolatine-3B-Instruct-DPO-Revised 1     7.9875
+Daredevil-8B                        1     7.8875
+Chocolatine-3B-Instruct-DPO-v1.0    1     7.6875
+NeuralDaredevil-8B-abliterated      1     7.6250
+Phi-3-mini-4k-instruct              1     7.2125
+Meta-Llama-3-8B-Instruct            1     7.1625
+vigostral-7b-chat                   1     6.7875
+Mistral-7B-Instruct-v0.3            1     6.7500
+Mistral-7B-Instruct-v0.2            1     6.2875
+########## Second turn ##########
+                                             score
+model                               turn
+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
+Meta-Llama-3-8B-Instruct            2     6.800000
+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
+########## Average ##########
+                                        score
+model
+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
+Meta-Llama-3-8B-Instruct             6.981250
+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
+```
+### 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