mobiuslabsgmbh
/

Qwen2.5-7B-Instruct-1M_4bitgs64_hqq_hf

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 ---
+license: apache-2.0
+train: false
+inference: false
+pipeline_tag: text-generation
 ---
+This is an <a href="https://github.com/mobiusml/hqq/">HQQ</a> all 4-bit (group-size=64) quantized <a href="https://huggingface.co/Qwen/Qwen2.5-7B-Instruct"> Qwen2.5-7B-Instruct</a> model.
+## Usage
+First, install the dependecies:
+```
+pip install git+https://github.com/mobiusml/hqq.git;
+pip install git+https://github.com/mobiusml/gemlite.git; #to use the gemlite backend
+pip install bitblas #to use the bitblas backend
+```
+Then you can use the sample code below:
+``` Python
+import torch
+device        = 'cuda:0'
+backend       = 'torchao_int4' #'torchao_int4' #"torchao_int4" (4-bit only) or "bitblas" (4-bit + 2-bit) or "gemlite" (8-bit, 4-bit, 2-bit, 1-bit)
+compute_dtype = torch.bfloat16 if backend=="torchao_int4" else torch.float16
+cache_dir     = None
+model_id      = 'mobiuslabsgmbh/Qwen2.5-7B-Instruct-1M_4bitgs64_hqq_hf'
+is_prequantized = 'hqq_hf' in model_id
+########################################################################
+#Load model
+from transformers import AutoModelForCausalLM, AutoTokenizer, HqqConfig
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=compute_dtype,
+    cache_dir=cache_dir,
+    device_map=device,
+    attn_implementation="sdpa",
+    low_cpu_mem_usage=True,
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir=cache_dir)
+#Save model before patching
+# model.save_pretrained(saved_quant_model)
+# tokenizer.save_pretrained(saved_quant_model)
+#Patching
+from hqq.utils.patching import prepare_for_inference
+prepare_for_inference(model, backend=backend, verbose=True)
+#Load GemLite cache
+if(backend == 'gemlite'):
+    import gemlite
+    gemlite.core.GEMLITE_TRITON_RESTRICT_M = True
+    gemlite.core.GemLiteLinear.load_config('/tmp/gemlite_config.json')
+########################################################################
+# ##Inference Using a custom hqq generator - currently manual compile breaks with pre-quantized llama models :(
+# from hqq.utils.generation_hf import HFGenerator
+# gen = HFGenerator(model, tokenizer, max_new_tokens=1000, do_sample=True, compile=False).enable_cuda_graph()
+# out = gen.generate("Write an essay about large language models.", print_tokens=True)
+########################################################################
+#Inference with model,generate()
+from hqq.utils.generation_hf import patch_model_for_compiled_runtime
+patch_model_for_compiled_runtime(model, tokenizer)
+prompt  = "Write an essay about large language models."
+inputs  = tokenizer.apply_chat_template([{"role":"user", "content":prompt}], tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True)
+outputs = model.generate(**inputs.to(model.device), max_new_tokens=1000, cache_implementation="static", pad_token_id=tokenizer.pad_token_id)
+#print(tokenizer.decode(outputs[0])
+########################################################################
+#Save gemlite cache
+if(backend == 'gemlite'):
+    gemlite.core.GemLiteLinear.cache_config('/tmp/gemlite_config.json')
+```