metadata
license: apache-2.0
datasets:
- PygmalionAI/PIPPA
- Norquinal/claude_multiround_chat_30k
- ehartford/dolphin
- cais/mmlu
- OpenLeecher/Teatime
- BAAI/COIG-PC
- natural_questions
RWKV 14B one state model
finetuend on instruction datasets ,can do Role play, for openllm leaderboard, impoved mmlu training datasets
this is a huggingface formatted model
checkpoint can be founded here https://huggingface.co/xiaol/Model_zoo/blob/main/rwkv-raven-14B-v4-one-state.pth and need to use new vocabs file https://huggingface.co/xiaol/Model_zoo/blob/main/20B_tokenizer_new_inference.json
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
#model_id = "xiaol/Huggingface-RWKV-claude-for-mobile-v4-world-1.5B-16k"
model_id = "xiaol/RWKV-raven-14B-one-state"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)
#model = model.half() #1.5B need fp32
#model = torch.compile(model) #need pytorch 2.0 and linux
model.to(0)
tokenizer = AutoTokenizer.from_pretrained(model_id)
question = "Tell me about ravens"
prompt = f"### Instruction: {question}\n### Response:"
inputs = tokenizer(prompt, return_tensors="pt").to(0)
output = model.generate(inputs["input_ids"], max_new_tokens=100)
print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
Traning details
https://wandb.ai/one-/out14B-one/runs/uhomhbgg/workspace