Then run the following code. config.json has been default to a sequence length of 8192, but you can also configure this in your Python code.
The provided modelling code, activated with trust_remote_code=True will automatically set the scale parameter from the configured max_position_embeddings. Eg for 8192, scale is set to 4.
python
1from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM, pipeline
2import argparse
34model_name_or_path ="TheBloke/WizardLM-13B-V1-1-SuperHOT-8K-fp16"56use_triton =False78tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)910config = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=True)11# Change this to the sequence length you want12config.max_position_embeddings =81921314model = AutoModelForCausalLM.from_pretrained(model_name_or_path,15 config=config,16 trust_remote_code=True,17 device_map='auto')1819# Note: check to confirm if this is correct prompt template is correct for this model!20prompt ="Tell me about AI"21prompt_template=f'''USER: {prompt}22ASSISTANT:'''2324print("\n\n*** Generate:")2526input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()27output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)28print(tokenizer.decode(output[0]))2930# Inference can also be done using transformers' pipeline3132print("*** Pipeline:")33pipe = pipeline(34"text-generation",35 model=model,36 tokenizer=tokenizer,37 max_new_tokens=512,38 temperature=0.7,39 top_p=0.95,40 repetition_penalty=1.1541)4243print(pipe(prompt_template)[0]['generated_text'])
Using other UIs: monkey patch
Provided in the repo is llama_rope_scaled_monkey_patch.py, written by @kaiokendev.
It can be theoretically be added to any Python UI or custom code to enable the same result as trust_remote_code=True. I have not tested this, and it should be superseded by using trust_remote_code=True, but I include it for completeness and for interest.
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Original model card: Kaio Ken's SuperHOT 8K
SuperHOT Prototype 2 w/ 8K Context
This is a second prototype of SuperHOT, a NSFW focused LoRA, this time 7B with 8K context and no RLHF, using the same technique described in the github blog.
Looking for Merged & Quantized Models?
Make some please :)
Using the monkey-patch?
You will NEED to apply the monkeypatch or, if you are already using the monkeypatch, change the scaling factor to 0.25 and the maximum sequence length to 8192
The monkeypatch is only necessary if you are using a front-end/back-end that does not already support scaling and said front-end/back-end is Python-based (i.e. Huggingface Transformers). To apply the patch, you will need to copy the llama_rope_scaled_monkey_patch.py into your working directory and call the exported function replace_llama_rope_with_scaled_rope at the very start of your Python program. It will modify the Transformers library's implementation of RoPE to properly apply the scaling factor.
Using Oobabooga with Exllama?
Switch your loader to exllama or exllama_hf Add the arguments max_seq_len 8192 and compress_pos_emb 4. While the model may work well with compress_pos_emb 2, it was trained on 4, so that is what I advocate for you to use
In the UI, you will see the loader option in the Models tab. Once you select either exllama or exllama_hf, the max_seq_len and compress_pos_emb settings will appear.
Training Details
I trained the LoRA with the following configuration:
1200 samples (~400 samples over 2048 sequence length)
learning rate of 3e-4
3 epochs
The exported modules are:
q_proj
k_proj
v_proj
o_proj
no bias
Rank = 4
Alpha = 8
no dropout
weight decay of 0.1
AdamW beta1 of 0.9 and beta2 0.99, epsilon of 1e-5
Trained on 4-bit base model
Cutoff length: 4096
Original model card: WizardLM's WizardLM 13B V1.1
This is the Full-Weight of WizardLM-13B V1.1 model.
🔥🔥🔥 [7/7/2023] The WizardLM-13B-V1.1 achieves 6.74 on MT-Bench Leaderboard, 86.32% on AlpacaEval Leaderboard, and 99.3% on WizardLM Eval. (Note: MT-Bench and AlpacaEval are all self-test, will push update and request review. All tests are completed under their official settings.)
