Update README.md

README.md

@@ -8,188 +8,21 @@ language:
 - hi
 - es
 - th
-library_name: transformers
-base_model: meta-llama/Meta-Llama-3.1-70B
 license: llama3.1
+base_model: meta-llama/Meta-Llama-3.1-8B
 pipeline_tag: text-generation
 tags:
 - facebook
 - meta
-- pytorch
 - llama
 - llama-3
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-extra_gated_fields:
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+
 ---
 
+Exl2 quant version using [exllamav2 0.1.9](https://github.com/turboderp/exllamav2/releases/tag/v0.1.9)
+
+Update 23/08 - it now includes all the fixes for the tokenizer and chat templates+tool calling
+
 ## Model Information
 
 The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
@@ -290,7 +123,7 @@ Where to send questions or comments about the model Instructions on how to provi
 
 ## How to use
 
-This repository contains two versions of Meta-Llama-3.1-70B-Instruct, for use with transformers and with the original `llama` codebase.
+This repository contains two versions of Meta-Llama-3.1-8B-Instruct, for use with transformers and with the original `llama` codebase.
 
 ### Use with transformers
 
@@ -298,13 +131,11 @@ Starting with `transformers >= 4.43.0` onward, you can run conversational infere
 
 Make sure to update your transformers installation via `pip install --upgrade transformers`.
 
-See the snippet below for usage with Transformers:
-
 ```python
 import transformers
 import torch
 
-model_id = "meta-llama/Meta-Llama-3.1-70B-Instruct"
+model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
 
 pipeline = transformers.pipeline(
     "text-generation",
@@ -325,6 +156,8 @@ outputs = pipeline(
 print(outputs[0]["generated_text"][-1])
 ```
 
+Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
+
 ### Tool use with transformers
 
 LLaMA-3.1 supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/).
@@ -371,44 +204,16 @@ After that, you can `generate()` again to let the model use the tool result in t
 see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).
 
 
-### Use with `bitsandbytes`
-
-The model checkpoints can be used in `8-bit` and `4-bit` for further memory optimisations using `bitsandbytes` and `transformers`
-
-See the snippet below for usage:
-
-```python
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_id = "meta-llama/Meta-Llama-3.1-70B-Instruct"
-quantization_config = BitsAndBytesConfig(load_in_8bit=True)
-
-quantized_model = AutoModelForCausalLM.from_pretrained(
-	model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config)
-
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-input_text = "What are we having for dinner?"
-input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
-
-output = quantized_model.generate(**input_ids, max_new_tokens=10)
-
-print(tokenizer.decode(output[0], skip_special_tokens=True))
-```
-
-To load in 4-bit simply pass `load_in_4bit=True`
-
 ### Use with `llama`
 
-Please, follow the instructions in the [repository](https://github.com/meta-llama/llama).
+Please, follow the instructions in the [repository](https://github.com/meta-llama/llama)
 
 To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
 
 ```
-huggingface-cli download meta-llama/Meta-Llama-3.1-70B-Instruct --include "original/*" --local-dir Meta-Llama-3.1-70B-Instruct
+huggingface-cli download meta-llama/Meta-Llama-3.1-8B-Instruct --include "original/*" --local-dir Meta-Llama-3.1-8B-Instruct
 ```
 
-
 ## Hardware and Software
 
 **Training Factors** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.
@@ -1286,4 +1091,4 @@ Finally, we put in place a set of resources including an [output reporting mecha
 
 The core values of Llama 3.1 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.1 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. 
 
-But Llama 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
+But Llama 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.