Fine-Tuned
Collection
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This model is a fine-tune (DPO) of microsoft/Phi-3-mini-4k-instruct
model.
All GGUF models are available here: MaziyarPanahi/calme-2.2-phi3-4b-GGUF
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 23.21 |
IFEval (0-Shot) | 50.69 |
BBH (3-Shot) | 37.73 |
MATH Lvl 5 (4-Shot) | 2.34 |
GPQA (0-shot) | 9.51 |
MuSR (0-shot) | 7.70 |
MMLU-PRO (5-shot) | 31.27 |
Metric | Value |
---|---|
Avg. | 69.78 |
AI2 Reasoning Challenge (25-Shot) | 62.80 |
HellaSwag (10-Shot) | 80.76 |
MMLU (5-Shot) | 69.10 |
TruthfulQA (0-shot) | 59.97 |
Winogrande (5-shot) | 72.45 |
GSM8k (5-shot) | 73.62 |
This model uses ChatML
prompt template:
<|im_start|>system
{System}
<|im_end|>
<|im_start|>user
{User}
<|im_end|>
<|im_start|>assistant
{Assistant}
You can use this model by using MaziyarPanahi/calme-2.2-phi3-4b
as the model name in Hugging Face's
transformers library.
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from transformers import pipeline
import torch
model_id = "MaziyarPanahi/calme-2.2-phi3-4b"
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
# attn_implementation="flash_attention_2"
)
tokenizer = AutoTokenizer.from_pretrained(
model_id,
trust_remote_code=True
)
streamer = TextStreamer(tokenizer)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
# this should work perfectly for the model to stop generating
terminators = [
tokenizer.eos_token_id, # this should be <|im_end|>
tokenizer.convert_tokens_to_ids("<|assistant|>"), # sometimes model stops generating at <|assistant|>
tokenizer.convert_tokens_to_ids("<|end|>") # sometimes model stops generating at <|end|>
]
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
generation_args = {
"max_new_tokens": 500,
"return_full_text": False,
"temperature": 0.0,
"do_sample": False,
"streamer": streamer,
"eos_token_id": terminators,
}
output = pipe(messages, **generation_args)
print(output[0]['generated_text'])