SamChat
SamChat is a merge of the following models
🧩 Configuration
models:
- model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
# no params for base model
- model: Intel/neural-chat-7b-v3-3
parameters:
weight: 0.55
density: 0.46
- model: cognitivecomputations/samantha-mistral-7b
parameters:
weight: 0.64
density: 0.55
merge_method: dare_ties
base_model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
parameters:
normalize: true
int8_mask: true
dtype: float16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "fhai50032/SamChat"
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"])
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 61.68 |
AI2 Reasoning Challenge (25-Shot) | 62.20 |
HellaSwag (10-Shot) | 81.88 |
MMLU (5-Shot) | 59.70 |
TruthfulQA (0-shot) | 52.89 |
Winogrande (5-shot) | 72.14 |
GSM8k (5-shot) | 41.24 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard62.200
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard81.880
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard59.700
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard52.890
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard72.140
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard41.240