Create README.md

README.md

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+---
+datasets:
+- abideen/Cosmopedia-100k-pretrain
+tags:
+- Mistral
+- 1bit
+- bitnet
+- abideen
+---
+"""this is my first attempt at converting a model float16 quantized model to 1.5bit. i used alpindale/Mistral-7B-v0.2-hf for the base model and \n
+trained on: abideen/cosmopedia-100k-pretain dataset and used his google colab project to make this"""
+
+#EXAMPLE INFERENCE CODE FROM ABIDEEN'S COLAB PROJECT
+```
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.models.llama.modeling_llama import *
+# Load a pretrained BitNet model
+model = "liminerity/Bitnet-Mistral.0.2-70M"
+tokenizer = AutoTokenizer.from_pretrained(model)
+model = AutoModelForCausalLM.from_pretrained(model)
+
+
+def activation_quant(x):
+    scale = 127.0 / x.abs().max(dim=-1, keepdim=True).values.clamp_(min=1e-5)
+    y = (x * scale).round().clamp_(-128, 127)
+    y = y / scale
+    return y
+def weight_quant(w):
+    scale = 1.0 / w.abs().mean().clamp_(min=1e-5)
+    u = (w * scale).round().clamp_(-1, 1)
+    u = u / scale
+    return u
+
+class BitLinear(nn.Linear):
+    def forward(self, x):
+        w = self.weight # a weight tensor with shape [d, k]
+        x = x.to(w.device)
+        RMSNorm = LlamaRMSNorm(x.shape[-1]).to(w.device)
+        x_norm = RMSNorm(x)
+        # A trick for implementing Straight−Through−Estimator (STE) using detach()
+        x_quant = x_norm + (activation_quant(x_norm) - x_norm).detach()
+        w_quant = w + (weight_quant(w) - w).detach()
+        y = F.linear(x_quant, w_quant)
+        return y
+
+def convert_to_bitnet(model, copy_weights):
+    for name, module in model.named_modules():
+        # Replace linear layers with BitNet
+        if isinstance(module, LlamaSdpaAttention) or isinstance(module, LlamaMLP):
+            for child_name, child_module in module.named_children():
+                if isinstance(child_module, nn.Linear):
+                    bitlinear = BitLinear(child_module.in_features, child_module.out_features, child_module.bias is not None).to(device="cuda:0")
+                    if copy_weights:
+                        bitlinear.weight = child_module.weight
+                        if child_module.bias is not None:
+                            bitlinear.bias = child_module.bias
+                    setattr(module, child_name, bitlinear)
+        # Remove redundant input_layernorms
+        elif isinstance(module, LlamaDecoderLayer):
+            for child_name, child_module in module.named_children():
+                if isinstance(child_module, LlamaRMSNorm) and child_name == "input_layernorm":
+                    setattr(module, child_name, nn.Identity().to(device="cuda:0"))
+
+
+convert_to_bitnet(model, copy_weights=True)
+model.to(device="cuda:0")
+
+prompt = "What is Machine Learning?"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+generate_ids = model.generate(inputs.input_ids, max_length=50)
+tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+```