language:
- en
pipeline_tag: text-generation
tags:
- enigma
- valiant
- valiant-labs
- llama
- llama-3.1
- llama-3.1-instruct
- llama-3.1-instruct-8b
- llama-3
- llama-3-instruct
- llama-3-instruct-8b
- 8b
- code
- code-instruct
- python
- conversational
- chat
- instruct
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
datasets:
- sequelbox/Tachibana
- LDJnr/Pure-Dove
model_type: llama
license: llama3.1
Enigma is a code-instruct model built on Llama 3.1 8b.
- High quality code instruct performance within the Llama 3 Instruct chat format
- Finetuned on synthetic code-instruct data generated with Llama 3.1 405b. Find the current version of the dataset here!
Version
This is the 2024-08-10 release of Enigma for Llama 3.1 8b.
Help us and recommend Enigma to your friends! We're excited for more Enigma releases in the future.
Right now, we're working on more new Build Tools to come very soon, built on Llama 3.1 :)
Prompting Guide
Enigma uses the Llama 3.1 Instruct prompt format. The example script below can be used as a starting point for general chat:
import transformers
import torch
model_id = "ValiantLabs/Llama3.1-8B-Enigma"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are Enigma, a highly capable code assistant."},
{"role": "user", "content": "Can you explain virtualization to me?"}
]
outputs = pipeline(
messages,
max_new_tokens=1024,
)
print(outputs[0]["generated_text"][-1])
The Model
Enigma is built on top of Llama 3.1 8b Instruct, using code-instruct data to supplement code-instruct performance using Llama 3.1 Instruct prompt style.
Our current version of the Enigma code-instruct dataset is sequelbox/Tachibana, supplemented with a small selection of data from LDJnr/Pure-Dove for general chat consistency.
Enigma is created by Valiant Labs.
Check out our HuggingFace page for Shining Valiant 2 and our other Build Tools models for creators!
Follow us on X for updates on our models!
We care about open source. For everyone to use.
We encourage others to finetune further from our models.