Arcee
:tulip: The open source alignment toolkit for finetuning and deploying LLMs :tulip:
Finetuning Datasets
The Arcee toolkit contains routines for you to manage and generate finetuning datasets. The arcee toolkit supports supervised finetuning (SFT) aka intruction tuning.
✍️ Instruction Set ✍️
Instruction tuning datasets contain a series of prompt and completion examples.
Instruction sets can be loaded from a csv.
from arcee.data import InstructionSet
instruction_set = InstructionSet("./datasets/strip_api.csv")
Or downloaded from the Arcee platform
from arcee.data import InstructionSet
instruction_set = InstructionSet("https://app.arcee.ai/arcee/alpaca")
Self-Instruct Generation
Evolv-Instruct Genertation
Explain-Instruct Generation
Finetuning Models
HuggingFace
from arcee.models import LM
from arcee.data import Instuctions
lm = LM("falcon30b")
instructions = Instructions("./datasets/stripe-api.json")
lm.train(instructions)
lm.predict("Place an order for the LLM-9000 product for 100 USD to the card 3007200039992000")
OpenAI
Cohere
Together
Mosaic ML
LangChain Integration
from langchain import Arcee
#goes in llms/arcee.py
prompt_template = "Write a stripe API request for the following: {order}."
llm = Arcee(temperature=0)
llm_chain = LLMChain(
llm=llm,
prompt=PromptTemplate.from_template(prompt_template)
)
llm_chain("Place an order for the LLM-9000 product for 100 USD to the card 3007200039992000")
Domain Pretraining
Coming soon!
🗻 Arcee Platform 🗻
Arcee offers a platform for managing your proprietary language models in production. We offer a version of our platform hosted in our cloud as well as a deployable version that you can take on prem.
Authenticaiton
To use the arcee platform, visit https://app.arcee.com/api-settings and export ARCEE_API_KEY=******* in your environment.
Dataset Managemnt
View, search, and edit datasets.
instruction_set.upload("project-name")
Hosted Training
Hosted Inference
Lifecycle Management
Metadata
Release files for dallm 0.0.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| dallm-0.0.6.tar.gz | 9.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| dallm-0.0.6-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 21.1 kB
Release files / dallm-0.0.6.tar.gz
| Download URL | dallm-0.0.6.tar.gz |
|---|---|
| Size | 9.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
c63a8730b49fa4faff06403db49d281601d9a1ef6491e23c60074530a175d286
|
|
BLAKE2b-256 checksum How to use checksums |
9ebaa342d6a37dba0b745873f6aa48fd2305d8257951f9ecc443c3ab1250d60e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.8.0 pkginfo/1.9.6 readme-renderer/40.0 requests/2.31.0 requests-toolbelt/1.0.0 urllib3/2.0.4 tqdm/4.65.0 importlib-metadata/6.7.0 keyring/24.2.0 rfc3986/1.5.0 colorama/0.4.6 CPython/3.8.10
|
Release files / dallm-0.0.6-py3-none-any.whl
| Download URL | dallm-0.0.6-py3-none-any.whl |
|---|---|
| Size | 11.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
0c7ae75c312b0a3be1c12c44378ec4375c3e2ab8699d6642e644b2fcca600c54
|
|
BLAKE2b-256 checksum How to use checksums |
169ab09b6fe43b3762f4f662d3c36d319cac49fffb5e7c59886c240a75858170
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.8.0 pkginfo/1.9.6 readme-renderer/40.0 requests/2.31.0 requests-toolbelt/1.0.0 urllib3/2.0.4 tqdm/4.65.0 importlib-metadata/6.7.0 keyring/24.2.0 rfc3986/1.5.0 colorama/0.4.6 CPython/3.8.10
|