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--- |
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license: apache-2.0 |
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tags: |
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- moe |
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train: false |
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inference: false |
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pipeline_tag: text-generation |
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--- |
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## Mixtral-8x7B-Instruct-v0.1-hf-attn-4bit-moe-3bit-metaoffload-HQQ |
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This is a version of the |
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<a href="https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1"> Mixtral-8x7B-Instruct-v0.1 model</a> quantized with a mix of 4-bit and 3-bit via Half-Quadratic Quantization (HQQ). |
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More specifically, the attention layers are quantized to 4-bit and the experts are quantized to 3-bit. |
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Contrary to the <a href="https://huggingface.co/mobiuslabsgmbh/Mixtral-8x7B-Instruct-v0.1-hf-attn-4bit-moe-2bitgs8-metaoffload-HQQ"> 2bitgs8 model </a> that was designed to use less GPU memory, this one uses about 22GB for the folks who want to get better quality and use the maximum VRAM available on 24GB GPUs. |
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</p> |
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## Performance |
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| Models | Mixtral Original | HQQ quantized | |
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|-------------------|------------------|------------------| |
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| Runtime VRAM | 94 GB | <b> 22.3 GB</b> | |
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| ARC (25-shot) | 70.22 | 69.62 | |
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| Hellaswag (10-shot)| 87.63 | | |
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| MMLU (5-shot) | 71.16 | | |
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| TruthfulQA-MC2 | 64.58 | 62.63 | |
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| Winogrande (5-shot)| 81.37 | 81.06 | |
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| GSM8K (5-shot)| 60.73 | | |
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| Average| 72.62 | | |
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### Basic Usage |
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To run the model, install the HQQ library from https://github.com/mobiusml/hqq and use it as follows: |
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``` Python |
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import transformers |
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from threading import Thread |
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model_id = 'mobiuslabsgmbh/Mixtral-8x7B-Instruct-v0.1-hf-attn-4bit-moe-3bit-metaoffload-HQQ' |
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#Load the model |
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from hqq.engine.hf import HQQModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = HQQModelForCausalLM.from_quantized(model_id) |
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#Optional: set backend/compile |
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#You will need to install CUDA kernels apriori |
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# git clone https://github.com/mobiusml/hqq/ |
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# cd hqq/kernels && python setup_cuda.py install |
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from hqq.core.quantize import * |
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HQQLinear.set_backend(HQQBackend.ATEN_BACKPROP) |
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def chat_processor(chat, max_new_tokens=100, do_sample=True): |
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tokenizer.use_default_system_prompt = False |
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streamer = transformers.TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) |
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generate_params = dict( |
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tokenizer("<s> [INST] " + chat + " [/INST] ", return_tensors="pt").to('cuda'), |
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streamer=streamer, |
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max_new_tokens=max_new_tokens, |
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do_sample=do_sample, |
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top_p=0.90, |
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top_k=50, |
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temperature= 0.6, |
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num_beams=1, |
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repetition_penalty=1.2, |
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) |
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t = Thread(target=model.generate, kwargs=generate_params) |
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t.start() |
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outputs = [] |
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for text in streamer: |
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outputs.append(text) |
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print(text, end="", flush=True) |
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return outputs |
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################################################################################################ |
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#Generation |
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outputs = chat_processor("How do I build a car?", max_new_tokens=1000, do_sample=False) |
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``` |
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### Quantization |
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You can reproduce the model using the following quant configs: |
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``` Python |
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from hqq.engine.hf import HQQModelForCausalLM, AutoTokenizer |
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model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" |
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model = HQQModelForCausalLM.from_pretrained(model_id, use_auth_token=hf_auth, cache_dir=cache_path) |
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#Quantize params |
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from hqq.core.quantize import * |
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attn_prams = BaseQuantizeConfig(nbits=4, group_size=64, offload_meta=True) |
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experts_params = BaseQuantizeConfig(nbits=3, group_size=64, offload_meta=True) |
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zero_scale_group_size = 128 |
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attn_prams['scale_quant_params']['group_size'] = zero_scale_group_size |
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attn_prams['zero_quant_params']['group_size'] = zero_scale_group_size |
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experts_params['scale_quant_params']['group_size'] = zero_scale_group_size |
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experts_params['zero_quant_params']['group_size'] = zero_scale_group_size |
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quant_config = {} |
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#Attention |
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quant_config['self_attn.q_proj'] = attn_prams |
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quant_config['self_attn.k_proj'] = attn_prams |
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quant_config['self_attn.v_proj'] = attn_prams |
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quant_config['self_attn.o_proj'] = attn_prams |
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#Experts |
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quant_config['block_sparse_moe.experts.w1'] = experts_params |
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quant_config['block_sparse_moe.experts.w2'] = experts_params |
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quant_config['block_sparse_moe.experts.w3'] = experts_params |
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#Quantize |
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model.quantize_model(quant_config=quant_config, compute_dtype=torch.float16); |
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model.eval(); |
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``` |
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