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Model: QuantFactory/Llama-3-13B-Instruct-v0.1-GGUF
Source: Original Platform

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README.md

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+
+---
+
+base_model: "meta-llama/Meta-Llama-3-8B-Instruct"
+library_name: transformers
+tags:
+  - mergekit
+  - merge
+  - facebook
+  - meta
+  - pytorch
+  - llama
+  - llama-3
+language:
+  - en
+pipeline_tag: text-generation
+license: other
+license_name: llama3
+license_link: LICENSE
+inference: false
+model_creator: MaziyarPanahi
+model_name: Llama-3-13B-Instruct-v0.1
+quantized_by: MaziyarPanahi
+
+---
+
+![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
+
+# QuantFactory/Llama-3-13B-Instruct-v0.1-GGUF
+This is quantized version of [MaziyarPanahi/Llama-3-13B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/Llama-3-13B-Instruct-v0.1) created using llama.cpp
+
+# Original Model Card
+
+
+<img src="./llama-3-merges.webp" alt="Goku 8x22B v0.1 Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+
+# Llama-3-13B-Instruct-v0.1
+
+This model is a self-merge of `meta-llama/Meta-Llama-3-8B-Instruct` model.
+
+# How to use
+
+You can use this model by using `MaziyarPanahi/Llama-3-13B-Instruct-v0.1` 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-3-13B-Instruct-v0.1"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    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("<|eot_id|>")
+]
+
+outputs = pipeline(
+    prompt,
+    max_new_tokens=256,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.95,
+)
+print(outputs[0]["generated_text"][len(prompt):])
+```
+
+## Prompt template
+
+```text
+<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+what's 25-4*2+3<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+To evaluate this expression, we need to follow the order of operations (PEMDAS):
+
+1. First, multiply 4 and 2: 4*2 = 8
+2. Then, subtract 8 from 25: 25 - 8 = 17
+3. Finally, add 3: 17 + 3 = 20
+
+So, 25-4*2+3 = 20!<|eot_id|>
+To evaluate this expression, we need to follow the order of operations (PEMDAS):
+
+1. First, multiply 4 and 2: 4*2 = 8
+2. Then, subtract 8 from 25: 25 - 8 = 17
+3. Finally, add 3: 17 + 3 = 20
+
+So, 25-4*2+3 = 20!
+```

configuration.json

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+{}