Update README.md

README.md

@@ -1,3 +1,67 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+```
+git clone https://github.com/casper-hansen/AutoAWQ.git   # latest source 2025-05-01
+cd AutoAWQ
+pip install -e .
+## go into AutoAWQ folder
+pip install --upgrade transformers
+
+## FOR STREAMING
+from awq import AutoAWQForCausalLM
+from transformers import AutoTokenizer, TextStreamer
+from awq.utils.utils import get_best_device
+
+device      = get_best_device()
+quant_path  = "Siddharth63/Qwen3-8B-base-AWQ"        # path or HF repo for the AWQ checkpoint
+
+# ---------- load model & tokenizer ----------
+model      = AutoAWQForCausalLM.from_quantized(quant_path, fuse_layers=True)
+tokenizer  = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True)
+streamer   = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+# ---------- tokenise & generate ----------
+input_ids = tokenizer("Atherosclerosis is", return_tensors="pt"
+).input_ids.to(device)
+
+_ = model.generate(
+        input_ids,
+        streamer        = streamer,
+        max_new_tokens =  512,        # full context window
+        use_cache       = True
+    )
+
+
+## FOR NON_STREAMING
+from awq import AutoAWQForCausalLM
+from transformers import AutoTokenizer, TextStreamer
+from awq.utils.utils import get_best_device
+
+device      = get_best_device()
+quant_path  = "Siddharth63/Qwen3-8B-base-AWQ"        # path or HF repo for the AWQ checkpoint
+
+# ---------- load model & tokenizer ----------
+model      = AutoAWQForCausalLM.from_quantized(quant_path, fuse_layers=True)
+tokenizer  = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True)
+
+input_ids = tokenizer(
+    "Atherosclerosis is",
+    return_tensors="pt"
+).input_ids.to(device)
+
+# ---------- generate (blocking) ----------
+output_ids = model.generate(
+    input_ids,
+    max_new_tokens=100,          # or max_length / temperature / etc.
+    use_cache=True               # default; speeds up incremental decoding
+)
+
+response = tokenizer.decode(
+    output_ids[0],
+    skip_special_tokens=True,    # drop <|im_start|> tokens
+)
+
+print("\n=== Model reply ===\n", response)
+```