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RichardErkhov/
johnsnowlabs_-_PhigRange-DPO-gguf

Name: johnsnowlabs_-_PhigRange-DPO-gguf
Availability: InStock

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gguf

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PhigRange-DPO - GGUF

Model creator: https://huggingface.co/johnsnowlabs/
Original model: https://huggingface.co/johnsnowlabs/PhigRange-DPO/

Name	Quant method	Size
PhigRange-DPO.Q2_K.gguf	Q2_K	1.03GB
PhigRange-DPO.IQ3_XS.gguf	IQ3_XS	1.12GB
PhigRange-DPO.IQ3_S.gguf	IQ3_S	1.16GB
PhigRange-DPO.Q3_K_S.gguf	Q3_K_S	1.16GB
PhigRange-DPO.IQ3_M.gguf	IQ3_M	1.23GB
PhigRange-DPO.Q3_K.gguf	Q3_K	1.33GB
PhigRange-DPO.Q3_K_M.gguf	Q3_K_M	1.33GB
PhigRange-DPO.Q3_K_L.gguf	Q3_K_L	1.47GB
PhigRange-DPO.IQ4_XS.gguf	IQ4_XS	1.43GB
PhigRange-DPO.Q4_0.gguf	Q4_0	1.49GB
PhigRange-DPO.IQ4_NL.gguf	IQ4_NL	1.5GB
PhigRange-DPO.Q4_K_S.gguf	Q4_K_S	1.51GB
PhigRange-DPO.Q4_K.gguf	Q4_K	1.62GB
PhigRange-DPO.Q4_K_M.gguf	Q4_K_M	1.62GB
PhigRange-DPO.Q4_1.gguf	Q4_1	1.65GB
PhigRange-DPO.Q5_0.gguf	Q5_0	1.8GB
PhigRange-DPO.Q5_K_S.gguf	Q5_K_S	1.8GB
PhigRange-DPO.Q5_K.gguf	Q5_K	1.87GB
PhigRange-DPO.Q5_K_M.gguf	Q5_K_M	1.87GB
PhigRange-DPO.Q5_1.gguf	Q5_1	1.95GB
PhigRange-DPO.Q6_K.gguf	Q6_K	2.13GB
PhigRange-DPO.Q8_0.gguf	Q8_0	2.75GB

Original model description:

license: cc-by-nc-4.0 base_model: PhigRange-2.7B-slerp tags:

generated_from_trainer
DPO
instruct
finetune
chatml
gpt4
synthetic data
distillation model-index:
name: PhigRange-DPO results: [] datasets:
mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha language:
en library_name: transformers pipeline_tag: text-generation

PhigRange-DPO

PhigRange-DPO is a DPO fine-tuned of johnsnowlabs/PhigRange-2.7B-Slerp using the mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha preference dataset. The model has been trained for for 1080 steps.

🏆 Evaluation results

Coming Soon

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "johnsnowlabs/PhigRange-DPO"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-04
train_batch_size: 1
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: AdamOptimizer32bit
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
training_steps: 1080

Framework versions

Transformers 4.38.0.dev0
Pytorch 2.1.2+cu118
Datasets 2.17.0
Tokenizers 0.15.0

RichardErkhov/johnsnowlabs_-_PhigRange-DPO-gguf