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RichardErkhov/
jpacifico_-_Chocolatine-14B-Instruct-DPO-v1.2-gguf

Name: jpacifico_-_Chocolatine-14B-Instruct-DPO-v1.2-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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

Model creator: https://huggingface.co/jpacifico/
Original model: https://huggingface.co/jpacifico/Chocolatine-14B-Instruct-DPO-v1.2/

Name	Quant method	Size
Chocolatine-14B-Instruct-DPO-v1.2.Q2_K.gguf	Q2_K	4.79GB
Chocolatine-14B-Instruct-DPO-v1.2.IQ3_XS.gguf	IQ3_XS	5.41GB
Chocolatine-14B-Instruct-DPO-v1.2.IQ3_S.gguf	IQ3_S	5.65GB
Chocolatine-14B-Instruct-DPO-v1.2.Q3_K_S.gguf	Q3_K_S	5.65GB
Chocolatine-14B-Instruct-DPO-v1.2.IQ3_M.gguf	IQ3_M	6.03GB
Chocolatine-14B-Instruct-DPO-v1.2.Q3_K.gguf	Q3_K	6.45GB
Chocolatine-14B-Instruct-DPO-v1.2.Q3_K_M.gguf	Q3_K_M	6.45GB
Chocolatine-14B-Instruct-DPO-v1.2.Q3_K_L.gguf	Q3_K_L	6.98GB
Chocolatine-14B-Instruct-DPO-v1.2.IQ4_XS.gguf	IQ4_XS	7.02GB
Chocolatine-14B-Instruct-DPO-v1.2.Q4_0.gguf	Q4_0	7.35GB
Chocolatine-14B-Instruct-DPO-v1.2.IQ4_NL.gguf	IQ4_NL	7.41GB
Chocolatine-14B-Instruct-DPO-v1.2.Q4_K_S.gguf	Q4_K_S	7.41GB
Chocolatine-14B-Instruct-DPO-v1.2.Q4_K.gguf	Q4_K	7.98GB
Chocolatine-14B-Instruct-DPO-v1.2.Q4_K_M.gguf	Q4_K_M	7.98GB
Chocolatine-14B-Instruct-DPO-v1.2.Q4_1.gguf	Q4_1	8.16GB
Chocolatine-14B-Instruct-DPO-v1.2.Q5_0.gguf	Q5_0	8.96GB
Chocolatine-14B-Instruct-DPO-v1.2.Q5_K_S.gguf	Q5_K_S	8.96GB
Chocolatine-14B-Instruct-DPO-v1.2.Q5_K.gguf	Q5_K	9.38GB
Chocolatine-14B-Instruct-DPO-v1.2.Q5_K_M.gguf	Q5_K_M	9.38GB
Chocolatine-14B-Instruct-DPO-v1.2.Q5_1.gguf	Q5_1	9.76GB
Chocolatine-14B-Instruct-DPO-v1.2.Q6_K.gguf	Q6_K	10.67GB
Chocolatine-14B-Instruct-DPO-v1.2.Q8_0.gguf	Q8_0	13.82GB

Original model description:

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 (14B params)
using the 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

4-bit quantized version is available here : jpacifico/Chocolatine-14B-Instruct-DPO-v1.2-Q4_K_M-GGUF

OpenLLM Leaderboard

Chocolatine is the best-performing 14B model on the OpenLLM Leaderboard (2024/09/01)
and even the number one of the < 22B params models

Metric	Value
Avg.	33.3
IFEval	68.52
BBH	49.85
MATH Lvl 5	17.98
GPQA	10.07
MuSR	12.35
MMLU-PRO	41.07

MT-Bench-French

Chocolatine-14B-Instruct-DPO-v1.2 outperforms its previous versions and its base model Phi-3-medium-4k-instruct on MT-Bench-French, used with multilingual-mt-bench and GPT-4-Turbo as LLM-judge.

########## First turn ##########
                                             score
model                                 turn        
gpt-4o-mini                           1     9.2875
Chocolatine-14B-Instruct-4k-DPO       1     8.6375
Chocolatine-14B-Instruct-DPO-v1.2     1     8.6125
Phi-3.5-mini-instruct                 1     8.5250
Chocolatine-3B-Instruct-DPO-v1.2      1     8.3750
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
Meta-Llama-3.1-8B-Instruct            1     7.0500
vigostral-7b-chat                     1     6.7875
Mistral-7B-Instruct-v0.3              1     6.7500
gemma-2-2b-it                         1     6.4500
French-Alpaca-7B-Instruct_beta        1     5.6875
vigogne-2-7b-chat                     1     5.6625

########## 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
Chocolatine-3B-Instruct-DPO-v1.2      2     7.862500
Phi-3-medium-4k-instruct              2     7.750000
Chocolatine-14B-Instruct-4k-DPO       2     7.737500
gpt-3.5-turbo                         2     7.679167
Phi-3.5-mini-instruct                 2     7.575000
Daredevil-8B                          2     7.087500
Meta-Llama-3.1-8B-Instruct            2     6.787500
Mistral-7B-Instruct-v0.3              2     6.500000
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

########## Average ##########
                                          score
model                                          
gpt-4o-mini                            9.100000
Chocolatine-14B-Instruct-DPO-v1.2      8.475000
Chocolatine-14B-Instruct-4k-DPO        8.187500
Chocolatine-3B-Instruct-DPO-v1.2       8.118750
Phi-3.5-mini-instruct                  8.050000
Phi-3-medium-4k-instruct               7.987500
Chocolatine-3B-Instruct-DPO-Revised    7.962500
gpt-3.5-turbo                          7.908333
Daredevil-8B                           7.487500
Meta-Llama-3.1-8B-Instruct             6.918750
Mistral-7B-Instruct-v0.3               6.625000
vigostral-7b-chat                      6.475000
gemma-2-2b-it                          6.275000
French-Alpaca-7B-Instruct_beta         5.587866
vigogne-2-7b-chat                      4.218750

Usage

You can run this model using my Colab notebook

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

RichardErkhov/jpacifico_-_Chocolatine-14B-Instruct-DPO-v1.2-gguf