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README.md
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---
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- enigma
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- valiant
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- valiant-labs
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- llama
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- llama-3.1
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- llama-3.1-instruct
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- llama-3.1-instruct-8b
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- llama-3
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- llama-3-instruct
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- llama-3-instruct-8b
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- 8b
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- code
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- code-instruct
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- python
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- conversational
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- chat
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- instruct
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base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
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datasets:
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- sequelbox/Tachibana
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- sequelbox/Supernova
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model_type: llama
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license: llama3.1
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---
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## Description
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This repo contains GGUF format model files for Llama3.1-8B-Enigma.
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## Files Provided
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| Name | Quant | Bits | File Size | Remark |
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| ---------------------------- | ----- | ---- | --------- | -------------------------------- |
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| llama3.1-8b-enigma.Q2_K.gguf | Q2_K | 2 | 3.18 GB | 2.96G, +3.5199 ppl @ Llama-3-8B |
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| llama3.1-8b-enigma.Q3_K.gguf | Q3_K | 3 | 4.02 GB | 3.74G, +0.6569 ppl @ Llama-3-8B |
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| llama3.1-8b-enigma.Q4_0.gguf | Q4_0 | 4 | 4.66 GB | 4.34G, +0.4685 ppl @ Llama-3-8B |
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| llama3.1-8b-enigma.Q4_K.gguf | Q4_K | 4 | 4.92 GB | 4.58G, +0.1754 ppl @ Llama-3-8B |
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| llama3.1-8b-enigma.Q5_K.gguf | Q5_K | 5 | 5.73 GB | 5.33G, +0.0569 ppl @ Llama-3-8B |
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| llama3.1-8b-enigma.Q6_K.gguf | Q6_K | 6 | 6.60 GB | 6.14G, +0.0217 ppl @ Llama-3-8B |
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| llama3.1-8b-enigma.Q8_0.gguf | Q8_0 | 8 | 8.54 GB | 7.96G, +0.0026 ppl @ Llama-3-8B |
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## Parameters
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| path | type | architecture | rope_theta | sliding_win | max_pos_embed |
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| ------------------------------ | ----- | ---------------- | ---------- | ----------- | ------------- |
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| ValiantLabs/Llama3.1-8B-Enigma | llama | LlamaForCausalLM | 500000.0 | null | 131072 |
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# Original Model Card
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![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64f267a8a4f79a118e0fcc89/it7MY5MyLCLpFQev5dUis.jpeg)
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Enigma is a code-instruct model built on Llama 3.1 8b.
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- High quality code instruct performance within the Llama 3 Instruct chat format
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- Finetuned on synthetic code-instruct data generated with Llama 3.1 405b. [Find the current version of the dataset here!](https://huggingface.co/datasets/sequelbox/Tachibana)
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- Overall chat performance supplemented with [generalist synthetic data.](https://huggingface.co/datasets/sequelbox/Supernova)
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## Version
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This is the **2024-09-04** release of Enigma for Llama 3.1 8b, enhancing code-instruct and general chat capabilities.
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Help us and recommend Enigma to your friends! We're excited for more Enigma releases in the future.
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Right now, we're working on more new Build Tools to come very soon, built on Llama 3.1 :)
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## Prompting Guide
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Enigma uses the [Llama 3.1 Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) prompt format. The example script below can be used as a starting point for general chat:
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```python
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import transformers
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import torch
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model_id = "ValiantLabs/Llama3.1-8B-Enigma"
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pipeline = transformers.pipeline(
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"text-generation",
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model=model_id,
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model_kwargs={"torch_dtype": torch.bfloat16},
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "You are Enigma, a highly capable code assistant."},
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{"role": "user", "content": "Can you explain virtualization to me?"}
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]
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outputs = pipeline(
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messages,
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max_new_tokens=1024,
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)
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print(outputs[0]["generated_text"][-1])
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```
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## The Model
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Enigma is built on top of Llama 3.1 8b Instruct, using high quality code-instruct data and general chat data in Llama 3.1 Instruct prompt style to supplement overall performance.
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Our current version of Enigma is trained on code-instruct data from [sequelbox/Tachibana](https://huggingface.co/datasets/sequelbox/Tachibana) and general chat data from [sequelbox/Supernova.](https://huggingface.co/datasets/sequelbox/Supernova)
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![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63444f2687964b331809eb55/VCJ8Fmefd8cdVhXSSxJiD.jpeg)
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Enigma is created by [Valiant Labs.](http://valiantlabs.ca/)
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[Check out our HuggingFace page for Shining Valiant 2 and our other Build Tools models for creators!](https://huggingface.co/ValiantLabs)
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[Follow us on X for updates on our models!](https://twitter.com/valiant_labs)
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We care about open source.
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For everyone to use.
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We encourage others to finetune further from our models.
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