Update model card

README.md

@@ -1,4 +1,24 @@
 ---
+tags:
+- finetuned
+- quantized
+- 4-bit
+- AWQ
+- transformers
+- pytorch
+- mistral
+- instruct
+- text-generation
+- conversational
+- license:apache-2.0
+- autotrain_compatible
+- endpoints_compatible
+- text-generation-inference
+- finetune
+- chatml
+model-index:
+  - name: WestLake-7B-v2
+    results: []
 base_model: senseable/WestLake-7B-v2
 license: apache-2.0
 language:
@@ -34,6 +54,54 @@ This repo contains AWQ model files for [Common Sense's WestLake 7B v2](https://h
 
 These files were quantised using hardware kindly provided by [SolidRusT Networks](https://solidrust.net/).
 
+## How to use
+
+### Install the necessary packages
+
+```bash
+pip install --upgrade autoawq autoawq-kernels
+```
+
+### Example Python code
+
+```python
+from awq import AutoAWQForCausalLM
+from transformers import AutoTokenizer, TextStreamer
+
+model_path = "solidrust/WestLake-7B-v2-AWQ"
+system_message = "You are Senzu, incarnated as a powerful AI."
+
+# Load model
+model = AutoAWQForCausalLM.from_quantized(model_path,
+                                          fuse_layers=True)
+tokenizer = AutoTokenizer.from_pretrained(model_path,
+                                          trust_remote_code=True)
+streamer = TextStreamer(tokenizer,
+                        skip_prompt=True,
+                        skip_special_tokens=True)
+
+# Convert prompt to tokens
+prompt_template = """\
+<|im_start|>system
+{system_message}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant"""
+
+prompt = "You're standing on the surface of the Earth. "\
+        "You walk one mile south, one mile west and one mile north. "\
+        "You end up exactly where you started. Where are you?"
+
+tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
+                  return_tensors='pt').input_ids.cuda()
+
+# Generate output
+generation_output = model.generate(tokens,
+                                  streamer=streamer,
+                                  max_new_tokens=512)
+
+```
+
 ### About AWQ
 
 AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
@@ -57,13 +125,3 @@ It is supported by:
 {prompt}<|im_end|>
 <|im_start|>assistant
 ```
-
-Also working with Basic Mistral format:
-
-```plaintext
-<|system|>
-</s>
-<|user|>
-{prompt}</s>
-<|assistant|>
-```