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---
base_model: internlm/internlm2-chat-20b
---
# internlm2-chat-20b-llama
[`internlm/internlm2-chat-20b`](https://huggingface.co/internlm/internlm2-chat-20b) weights formatted to match standard Llama modeling code.
Model can be loaded directly, but for tokenizer use `trust_remote_code`
# usage:
```py
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "kiranr/internlm2-chat-20b-llama"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto",
attn_implementation="flash_attention_2",
)
messages = [
{"role": "user", "content": "what is the square root of banana?"}
]
model_input = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
generated_ids = model.generate(
model_input,
max_new_tokens=1024,
do_sample=True,
eos_token_id=[92542, 2], # <|im_end|> and </s>
)
output = tokenizer.decode(
generated_ids[0][model_input.shape[-1] : -1], skip_special_tokens=True
)
print(output)
``` |