Update doc

README.md

@@ -11,49 +11,80 @@ tags:
 ---
 
 <p align="center" width="100%">
-<img src="https://huggingface.co/bofenghuang/vigogne-2-7b-chat/resolve/main/vigogne_logo.png" alt="Vigogne" style="width: 40%; min-width: 300px; display: block; margin: auto;">
+<img src="https://huggingface.co/bofenghuang/vigogne-2-7b-chat/resolve/v2.0/logo_v2.jpg" alt="Vigogne" style="width: 30%; min-width: 300px; display: block; margin: auto;">
 </p>
 
-# Vigogne-2-7B-Chat: A Llama-2 based French chat model
+# Vigogne-2-7B-Chat-V2.0: A Llama-2 based French chat LLM
 
-Vigogne-2-7B-Chat is a model based on [LLaMA-2-7B](https://ai.meta.com/llama) that has been fine-tuned to conduct multi-turn dialogues in French between human user and AI assistant.
+Vigogne-2-7B-Chat-V2.0 is a French chat LLM, based on [LLaMA-2-7B](https://ai.meta.com/llama), optimized to generate helpful and coherent responses in user conversations.
 
-For more information, please visit the Github repo: https://github.com/bofenghuang/vigogne
+Check out our [blog](https://github.com/bofenghuang/vigogne/blob/main/blogs/2023-08-17-vigogne-chat-v2_0.md) and [GitHub repository](https://github.com/bofenghuang/vigogne) for more information.
 
-**Usage and License Notices**: Vigogne-2-7B-Chat follows the same usage policy as Llama-2, which can be found [here](https://ai.meta.com/llama/use-policy).
+**Usage and License Notices**: Vigogne-2-7B-Chat-V2.0 follows Llama-2's [usage policy](https://ai.meta.com/llama/use-policy). A significant portion of the training data is distilled from GPT-3.5-Turbo and GPT-4, kindly use it cautiously to avoid any violations of OpenAI's [terms of use](https://openai.com/policies/terms-of-use).
+
+## Changelog
+
+All previous versions are accessible through branches.
+
+- **V1.0**: Trained on 420K chat data.
+- **V2.0**: Trained on 520K data. Check out our [blog](https://github.com/bofenghuang/vigogne/blob/main/blogs/2023-08-17-vigogne-chat-v2_0.md) for more details.
 
 ## Usage
 
 ```python
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, TextStreamer
 from vigogne.preprocess import generate_inference_chat_prompt
 
 model_name_or_path = "bofenghuang/vigogne-2-7b-chat"
-tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side="right", use_fast=False)
-model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, device_map="auto")
+revision = "v2.0"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, revision=revision, padding_side="right", use_fast=False)
+model = AutoModelForCausalLM.from_pretrained(model_name_or_path, revision=revision, torch_dtype=torch.float16, device_map="auto")
+
+streamer = TextStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
+
+
+def infer(
+    utterances,
+    system_message=None,
+    temperature=0.1,
+    top_p=1.0,
+    top_k=0,
+    repetition_penalty=1.1,
+    max_new_tokens=1024,
+    **kwargs,
+):
+    prompt = generate_inference_chat_prompt(utterances, tokenizer, system_message=system_message)
+    input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"].to(model.device)
+    input_length = input_ids.shape[1]
+
+    generated_outputs = model.generate(
+        input_ids=input_ids,
+        generation_config=GenerationConfig(
+            temperature=temperature,
+            do_sample=temperature > 0.0,
+            top_p=top_p,
+            top_k=top_k,
+            repetition_penalty=repetition_penalty,
+            max_new_tokens=max_new_tokens,
+            eos_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.pad_token_id,
+            **kwargs,
+        ),
+        streamer=streamer,
+        return_dict_in_generate=True,
+    )
+    generated_tokens = generated_outputs.sequences[0, input_length:]
+    generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=True)
+    return generated_text
+
 
 user_query = "Expliquez la différence entre DoS et phishing."
-prompt = generate_inference_chat_prompt([[user_query, ""]], tokenizer=tokenizer)
-input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"].to(model.device)
-input_length = input_ids.shape[1]
-
-generated_outputs = model.generate(
-    input_ids=input_ids,
-    generation_config=GenerationConfig(
-        temperature=0.1,
-        do_sample=True,
-        repetition_penalty=1.0,
-        max_new_tokens=512,
-    ),
-    return_dict_in_generate=True,
-)
-generated_tokens = generated_outputs.sequences[0, input_length:]
-generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=True)
-print(generated_text)
+infer([[user_query, ""]])
 ```
 
-You can infer this model by using the following Google Colab Notebook.
+You can utilize the Google Colab Notebook below for inferring with the Vigogne chat models.
 
 <a href="https://colab.research.google.com/github/bofenghuang/vigogne/blob/main/notebooks/infer_chat.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>