Create README.md (#2)

- Create README.md (a09141c4e518c0cf1ff073c339725b25a7412ca2)

README.md

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+---
+base_model: winglian/Llama-3-8b-64k-PoSE
+library_name: transformers
+tags:
+- axolotl
+- finetune
+- dpo
+- facebook
+- meta
+- pytorch
+- llama
+- llama-3
+- 64k
+- pose
+language:
+- en
+pipeline_tag: text-generation
+license: llama3
+license_name: llama3
+license_link: LICENSE
+inference: false
+model_creator: MaziyarPanahi
+model_name: Llama-3-8B-Instruct-64k
+quantized_by: MaziyarPanahi
+datasets:
+- Intel/orca_dpo_pairs
+---
+
+<img src="./llama-3-merges.webp" alt="Llama-3 DPO Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+
+
+# MaziyarPanahi/Llama-3-8B-Instruct-64k
+
+This model has been made based on a great of [@winglian](https://huggingface.co/winglian/) with his latest model [winglian/Llama-3-8b-64k-PoSE](https://huggingface.co/winglian/Llama-3-8b-64k-PoSE/)
+
+> This model uses [PoSE](https://huggingface.co/papers/2309.10400) to extend Llama's context length from 8k to 64k @ rope_theta: 500000.0. 
+> We used PoSE with continued pretraining on 300M tokens from the RedPajama V1 dataset using data between 6k-8k tokens.
+> We have further set rope_theta to 2M after continued pre-training to potentially further extend the context past 64k. 
+> This was trained on a subset of the RedPajama v1 dataset with text between 6k-8k context. We trained a rank stabilized LoRA of rank 256. [WandB](https://wandb.ai/oaaic/llama-3-64k/runs/tkcyjt37)
+
+# Quantized GGUF
+
+All GGUF models come with context length of `64000`: [MaziyarPanahi/Llama-3-8B-Instruct-64k-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-64k-GGUF)
+
+
+# How to use
+
+You can use this model by using `MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3` as the model name in Hugging Face's
+transformers library.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+from transformers import pipeline
+import torch
+
+model_id = "MaziyarPanahi/Llama-3-8B-Instruct-64k"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+    # attn_implementation="flash_attention_2"
+)
+
+tokenizer = AutoTokenizer.from_pretrained(
+    model_id,
+    trust_remote_code=True
+)
+
+streamer = TextStreamer(tokenizer)
+
+pipeline = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    streamer=streamer
+)
+
+# Then you can use the pipeline to generate text.
+
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
+
+prompt = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|im_end|>")
+]
+
+outputs = pipeline(
+    prompt,
+    max_new_tokens=8192,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.95,
+)
+print(outputs[0]["generated_text"][len(prompt):])
+```
+
+