L-MChat-7b
L-MChat-7b is a merge of the following models:
Configuration
slices:
- sources:
- model: Nexusflow/Starling-LM-7B-beta
layer_range: [0, 32]
- model: FuseAI/FuseChat-7B-VaRM
layer_range: [0, 32]
merge_method: slerp
base_model: FuseAI/FuseChat-7B-VaRM
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Artples/M-LChat-7b"
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"])
License
Apache 2.0 but you cannot use this model to directly compete with OpenAI.
How?
Usage of LazyMergekit.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 69.57 |
| AI2 Reasoning Challenge (25-Shot) | 65.61 |
| HellaSwag (10-Shot) | 84.59 |
| MMLU (5-Shot) | 65.44 |
| TruthfulQA (0-shot) | 50.94 |
| Winogrande (5-shot) | 81.37 |
| GSM8k (5-shot) | 69.45 |
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 21.02 |
| IFEval (0-Shot) | 52.97 |
| BBH (3-Shot) | 24.20 |
| MATH Lvl 5 (4-Shot) | 7.93 |
| GPQA (0-shot) | 7.38 |
| MuSR (0-shot) | 8.12 |
| MMLU-PRO (5-shot) | 25.54 |