Instructions to use lyogavin/Anima-7B-100K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lyogavin/Anima-7B-100K with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="lyogavin/Anima-7B-100K", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lyogavin/Anima-7B-100K", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("lyogavin/Anima-7B-100K", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lyogavin/Anima-7B-100K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lyogavin/Anima-7B-100K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lyogavin/Anima-7B-100K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lyogavin/Anima-7B-100K

SGLang

How to use lyogavin/Anima-7B-100K with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "lyogavin/Anima-7B-100K" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lyogavin/Anima-7B-100K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "lyogavin/Anima-7B-100K" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lyogavin/Anima-7B-100K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use lyogavin/Anima-7B-100K with Docker Model Runner:
```
docker model run hf.co/lyogavin/Anima-7B-100K
```

Anima LLM supporting 100K input token length. It's trained based on Llama2 7B, so the license support commercial use!

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.

How to train/infer?

install dependencies

# Please update the path of `CUDA_HOME`
export CUDA_HOME=/usr/local/cuda-11.8
pip install transformers==4.31.0
pip install sentencepiece
pip install ninja
pip install flash-attn --no-build-isolation
pip install git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary
pip install git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/xentropy
pip install evaluate
pip install git+https://github.com/huggingface/peft.git@v0.4.0
pip install wandb

inference

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

base_model = "lyogavin/Anima-7B-100K"
tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(
            base_model,
            torch_dtype=torch.float16,
            trust_remote_code=True,
            device_map="auto", 
        )
model.eval()

prompt = "Where is the capital of US?"
inputs = tokenizer(prompt, return_tensors="pt")

inputs['input_ids'] = inputs['input_ids'].cuda()
inputs['attention_mask'] = inputs['attention_mask'].cuda()

# Generate
generate_ids = model.generate(**inputs, max_new_tokens=30,
                       only_last_logit=True, # to save memory
                       use_cache=False, # when run into OOM, enable this can save memory
                       xentropy=True)
output = tokenizer.batch_decode(generate_ids, 
                                skip_special_tokens=True,
                                clean_up_tokenization_spaces=False)[0]

Training

./run_longer_training.sh

Evaluations

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.

1. longchat topic retrieval

Model	Accuracy
Claude2	0.9
together llama2 32k	0.15
longchat 32k 1.5	0.05
Anima 100K	0.5

2. longchat number retrieval

Model	Accuracy
Claude2	0.85
together llama2 32k	0.2
longchat 32k 1.5	0.05
Anima 100K	0.45

3. Narrative QA in zeroscore

Model	F1
Claude2	0.6187
together llama2 32k	0.3833
longchat 32k 1.5	0.2416
Anima 100K	0.4919

Github

Github repo is here

Downloads last month: 349

lyogavin
/

Anima-7B-100K