Update README.md

README.md

@@ -1,5 +1,29 @@
 ---
 license: mit
+base_model: jpacifico/Chocolatine-3B-Instruct-DPO-Revised
+pipeline_tag: text-generation
+inference: false
+model_creator: jpacifico
+model_name: Chocolatine-3B-Instruct-DPO-Revised
+model_type: phi3
+language:
+  - fr
+  - en
+datasets:
+- jpacifico/french-orca-dpo-pairs-revised
+library_name: transformers
+quantized_by: ThiloteE
+tags:
+  - text-generation-inference
+  - transformers
+  - GGUF
+  - GPT4All-community
+  - GPT4All
+  - conversational
+  - french
+  - chocolatine
+
+
 ---
 
 > [!NOTE]
@@ -14,7 +38,245 @@ license: mit
 - Static quants of https://huggingface.co/jpacifico/Chocolatine-3B-Instruct-DPO-Revised at commit [fa3e742](https://huggingface.co/jpacifico/Chocolatine-3B-Instruct-DPO-Revised/commit/fa3e742dd80b3f38127fb62f5fc66eaf468fb95c)
 - Quantized by [ThiloteE](https://huggingface.co/ThiloteE) with llama.cpp commit [e09a800](https://github.com/ggerganov/llama.cpp/commit/e09a800f9a9b19c73aa78e03b4c4be8ed988f3e6)
 
-# Notes
-
 These quants were created with a customized configuration that have been proven to not cause visible end of string (eos) tokens during inference with [GPT4All](https://www.nomic.ai/gpt4all).
-The config.json, generation_config.json and  tokenizer_config.json differ from the original configuration as can be found in the original model's repository at the time of creation of these quants.
+The config.json, generation_config.json and  tokenizer_config.json differ from the original configuration as can be found in the original model's repository at the time of creation of these quants.
+
+
+# Prompt Template (for GPT4All)
+
+Example System Prompt:
+```
+<|im_start|>system
+<|system|>
+Vous trouverez ci-dessous une instruction décrivant une tâche. Rédigez une réponse qui réponde de manière appropriée à la demande.<|end|><|im_end|>
+
+```
+
+Chat Template:
+```
+<|user|>
+%1<|end|>
+<|assistant|>
+%2<|end|>
+
+```
+
+# Context Length
+
+`4096`
+
+Use a lower value during inference, if you do not have enough RAM or VRAM.
+
+# Provided Quants
+
+
+| Link | Type | Size/GB | Notes |
+|:-----|:-----|--------:|:------|
+| [GGUF](https://huggingface.co/GPT4All-Community/Chocolatine-3B-Instruct-DPO-Revised-GGUF/resolve/main/Chocolatine-3B-Instruct-DPO-Revised-Q4_0.gguf?download=true) | Q4_0 | 2.44 | fast, recommended |
+
+
+
+
+# About GGUF
+
+If you are unsure how to use GGUF files, refer to one of [TheBloke's
+READMEs](https://huggingface.co/TheBloke/DiscoLM_German_7b_v1-GGUF) for
+more details, including on how to concatenate multi-part files.
+
+Here is a handy graph by ikawrakow comparing some quant types (lower is better):
+
+![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
+
+And here are Artefact2's thoughts on the matter:
+https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
+
+# Thanks
+
+I thank Mradermacher and TheBloke for Inspiration to this model card and their contributions to open source. Also 3Simplex for lots of help along the way.
+Shoutout to the GPT4All and llama.cpp communities :-)
+
+
+------
+
+<!-- footer end -->
+<!-- original-model-card start -->
+
+
+------
+------
+
+# Original Model card:
+
+---
+library_name: transformers
+license: mit
+language:
+- fr
+- en
+tags:
+- french
+- chocolatine
+datasets:
+- jpacifico/french-orca-dpo-pairs-revised
+pipeline_tag: text-generation
+---
+
+### Chocolatine-3B-Instruct-DPO-Revised  
+
+DPO fine-tuned of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) (3.82B 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
+
+Chocolatine is the best-performing 3B model on the [OpenLLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) (august 2024)   
+
+![image/png](https://github.com/jpacifico/Chocolatine-LLM/blob/main/Assets/openllm_choco3b_revised.png?raw=false)  
+
+
+|      Metric       |Value|
+|-------------------|----:|
+|**Avg.**               |**27.63**|
+|IFEval (0-Shot)    |56.23|
+|BBH (3-Shot)       |37.16|
+|MATH Lvl 5 (4-Shot)|14.5|
+|GPQA (0-shot)      |9.62|
+|MuSR (0-shot)      |15.1|
+|MMLU-PRO (5-shot)  |33.21|
+
+
+### MT-Bench-French
+
+Chocolatine-3B-Instruct-DPO-Revised is outperforming GPT-3.5-Turbo on [MT-Bench-French](https://huggingface.co/datasets/bofenghuang/mt-bench-french) by Bofeng Huang,  
+used with [multilingual-mt-bench](https://github.com/Peter-Devine/multilingual_mt_bench)  
+
+```
+########## First turn ##########
+                                           score
+model                               turn        
+gpt-3.5-turbo                       1     8.1375
+Chocolatine-3B-Instruct-DPO-Revised 1     7.9875
+Daredevil-8B                        1     7.8875
+Daredevil-8B-abliterated            1     7.8375
+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
+French-Alpaca-7B-Instruct_beta      1     5.6875
+vigogne-2-7b-chat                   1     5.6625
+vigogne-2-7b-instruct               1     5.1375
+
+########## Second turn ##########
+                                             score
+model                               turn          
+Chocolatine-3B-Instruct-DPO-Revised 2     7.937500
+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
+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
+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                                        
+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
+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
+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_3B_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'])
+```
+
+* **4-bit quantized version** is available here : [jpacifico/Chocolatine-3B-Instruct-DPO-Revised-Q4_K_M-GGUF](https://huggingface.co/jpacifico/Chocolatine-3B-Instruct-DPO-Revised-Q4_K_M-GGUF)
+
+* **Ollama**: [jpacifico/chocolatine-3b](https://ollama.com/jpacifico/chocolatine-3b)
+
+```bash
+ollama run jpacifico/chocolatine-3b
+```
+
+Ollama *Modelfile* example :
+
+```bash
+FROM ./chocolatine-3b-instruct-dpo-revised-q4_k_m.gguf
+TEMPLATE """{{ if .System }}<|system|>
+{{ .System }}<|end|>
+{{ end }}{{ if .Prompt }}<|user|>
+{{ .Prompt }}<|end|>
+{{ end }}<|assistant|>
+{{ .Response }}<|end|>
+"""
+PARAMETER stop """{"stop": ["<|end|>","<|user|>","<|assistant|>"]}"""
+SYSTEM """You are a friendly assistant called Chocolatine."""
+```
+
+### 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
+
+
+<!-- original-model-card end -->
+<!-- end -->