code-example-fixed

README.md

@@ -16,6 +16,13 @@ Code Llama is a collection of pretrained and fine-tuned generative text models r
 | 34B  | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) |
 | 70B  | [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-hf) | [codellama/CodeLlama-70b-Python-hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf) | [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) |
 
+Model capabilities:
+
+- [x] Code completion.
+- [ ] Infilling.
+- [x] Instructions / chat.
+- [ ] Python specialist.
+
 ## Model Use
 
 Install `transformers`
@@ -24,14 +31,60 @@ Install `transformers`
 pip install transformers accelerate
 ```
 
-**Warning:** The 70B Instruct model has a different prompt template than the smaller versions. We'll update this repo soon.
-
-Model capabilities:
+**Chat use:** The 70B Instruct model uses a different prompt template than the smaller versions. To use it with `transformers`, we recommend you use the built-in chat template:
+
+ ```py
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
+
+model_id = "codellama/CodeLlama-70b-Instruct-hf"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+ chat = [
+    {"role": "system", "content": "You are a helpful and honest code assistant expert in JavaScript. Please, provide all answers to programming questions in JavaScript"},
+    {"role": "user", "content": "Write a function that computes the set of sums of all contiguous sublists of a given list."},
+]
+inputs = tokenizer.apply_chat_template(chat, return_tensors="pt").to("cuda")
+
+output = model.generate(input_ids=inputs, max_new_tokens=200)
+output = output[0].to("cpu")
+print(tokenizer.decode(output))
+```
 
-- [x] Code completion.
-- [ ] Infilling.
-- [x] Instructions / chat.
-- [ ] Python specialist.
+ You can also use the model for **text or code completion**. This examples uses transformers' `pipeline` interface:
+
+ ```py
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model_id = "codellama/CodeLlama-70b-hf"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+ sequences = pipeline(
+    'def fibonacci(',
+    do_sample=True,
+    temperature=0.2,
+    top_p=0.9,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.eos_token_id,
+    max_length=100,
+)
+for seq in sequences:
+    print(f"Result: {seq['generated_text']}")
+```
 
 ## Model Details
 *Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).