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--- |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- llama2 |
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- 100k |
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- 7b |
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--- |
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Anima LLM supporting 100K input token length. It's trained based on Llama2 7B, so the license support commercial use! |
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We carefully curated long QA training dataset from 30k to 100k length to train this model. We also made a lot of memory optimizations to make it scale to 100k tokens. |
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## How to train/infer? |
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#### install dependencies |
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```bash |
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# Please update the path of `CUDA_HOME` |
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export CUDA_HOME=/usr/local/cuda-11.8 |
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pip install transformers==4.31.0 |
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pip install sentencepiece |
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pip install ninja |
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pip install flash-attn --no-build-isolation |
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pip install git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary |
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pip install git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/xentropy |
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pip install evaluate |
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pip install git+https://github.com/huggingface/peft.git@v0.4.0 |
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pip install wandb |
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``` |
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#### inference |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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base_model = "lyogavin/Anima-7B-100K" |
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tokenizer = AutoTokenizer.from_pretrained(base_model) |
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model = AutoModelForCausalLM.from_pretrained( |
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base_model, |
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torch_dtype=torch.float16, |
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trust_remote_code=True, |
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device_map="auto", |
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) |
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model.eval() |
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prompt = "Where is the capital of US?" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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inputs['input_ids'] = inputs['input_ids'].cuda() |
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inputs['attention_mask'] = inputs['attention_mask'].cuda() |
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# Generate |
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generate_ids = model.generate(**inputs, max_new_tokens=30, |
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only_last_logit=True, # to save memory |
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use_cache=False, # when run into OOM, enable this can save memory |
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xentropy=True) |
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output = tokenizer.batch_decode(generate_ids, |
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skip_special_tokens=True, |
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clean_up_tokenization_spaces=False)[0] |
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``` |
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#### Training |
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```bash |
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./run_longer_training.sh |
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``` |
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## Evaluations |
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There's almost none evaluation dataset designed for 100k tokens. So we designed/curated some dataset for this model. We compared this model and several other public/private models. |
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#### 1. longchat topic retrieval |
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| Model | Accuracy | |
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|-------------------|---------| |
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| Claude2 | 0.9 | |
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| together llama2 32k | 0.15 | |
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| longchat 32k 1.5 | 0.05 | |
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| Anima 100K | 0.5 | |
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#### 2. longchat number retrieval |
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| Model | Accuracy | |
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|-------------------|---------| |
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| Claude2 | 0.85 | |
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| together llama2 32k | 0.2 | |
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| longchat 32k 1.5 | 0.05 | |
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| Anima 100K | 0.45 | |
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#### 3. Narrative QA in zeroscore |
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| Model | F1 | |
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|-------------------|---------| |
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| Claude2 | 0.6187 | |
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| together llama2 32k | 0.3833 | |
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| longchat 32k 1.5 | 0.2416 | |
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| Anima 100K | 0.4919 | |
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## Github |
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Github repo is [here](https://github.com/lyogavin/Anima/tree/main/anima_100k) |
