Update README.md

README.md

@@ -1,3 +1,59 @@
 ---
 license: bigcode-openrail-m
+datasets:
+- WizardLM/WizardLM_evol_instruct_70k
+language:
+- en
 ---
+
+<font size=5>Here is an example to show how to use model quantized by auto_gptq</font>
+```
+_4BITS_MODEL_PATH_V1_ = 'GodRain/WizardCoder-15B-V1.1-4bit'
+
+# pip install auto_gptq
+from auto_gptq import AutoGPTQForCausalLM
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(_4BITS_MODEL_PATH_V1_)
+model = AutoGPTQForCausalLM.from_quantized(_4BITS_MODEL_PATH_V1_)
+
+out = evaluate("Hello, tell me a story about sun", model=model, tokenizer=tokenizer)
+print(out[0].strip())
+```
+
+```
+def evaluate(
+        batch_data,
+        tokenizer,
+        model,
+        temperature=1,
+        top_p=0.9,
+        top_k=40,
+        num_beams=1,
+        max_new_tokens=2048,
+        **kwargs,
+):
+    prompts = generate_prompt(batch_data)
+    inputs = tokenizer(prompts, return_tensors="pt", max_length=256, truncation=True)
+    input_ids = inputs["input_ids"].to(device)
+    generation_config = GenerationConfig(
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        num_beams=num_beams,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.pad_token_id,
+        **kwargs,
+    )
+    with torch.no_grad():
+        generation_output = model.generate(
+            input_ids=input_ids,
+            generation_config=generation_config,
+            return_dict_in_generate=True,
+            output_scores=True,
+            max_new_tokens=max_new_tokens,
+        )
+    s = generation_output.sequences
+    output = tokenizer.batch_decode(s, skip_special_tokens=True)
+    return output
+```