license: apache-2.0
datasets:
- Anthropic/hh-rlhf
- OpenAssistant/oasst1
- databricks/databricks-dolly-15k
language:
- en
- fr
- de
- es
- it
Model Card for Alfred-40B-0723
Alfred-40B-0723 is a finetuned version of Falcon-40B, obtained with Reinforcement Learning from Human Feedback (RLHF).
It is the first of a series of RLHF models based on Falcon-40B that will be regularly released. It is made available under the Apache 2.0 License.
Model Details
Model Description
- Developed by: LightOn
- Model type: Causal decoder-only;
- Language(s) (NLP): English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish);
- License: Apache 2.0 license.
- Finetuned from model: Falcon-40B
Uses
Direct Use
Alfred-40B-0723 can be used as an instruct or chat model. We encourage its usage for research on large language models finetuned with RLHF as well.
Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
Bias, Risks, and Limitations
Alfred-40B-0723 is a finetune of Falcon-40B. As such, it is trained mostly on English, German, Spanish, French, with limited capabilities also in in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish. It will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
Recommendations
We recommend users of Alfred-40B-0723 to implement appropriate guardrails and precautions in any production use.
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "lightonai/alfred-40b-0723"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Write a short text to announce that the new transformer model Alfred is available in open-source on Huggingface, include emojis.",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
Training Details
Training Data
Alfred-40B-0723 was trained on a mixture of publicly available and in-house curated datasets.
momentum-internal is a collection of prompts rated as gold quality from the staff of LightOn in their daily workflow.
Training Procedure
Alfred-40B-0723 was trained on 128 A100 40GB GPUs, using a 3D parallelism strategy (TP=8, PP=4, DP=4) combined with ZeRO. The value model is initialized from the reward model and does not have any shared parameters with the policy network.
Preprocessing
Samples from each of the datasets have been programmatically formatted to chat, instructions and few-shot promtps.
Training Hyperparameters
Policy and Value Optimizer Config
| Hyperparameter | Value | Comment |
|---|---|---|
| Precision | bfloat16 |
|
| Optimizer | AdamW | |
| Learning rate | 1.85e-6 | 10 warm-up steps, cosine decay over a 100 steps to 1.85e-7 |
Trainer config
| Hyperparameter | Value |
|---|---|
| Num Rollouts | 1024 |
| Policy Epochs | 1 |
| Value Epochs | 1 |
| KL Coef | 0.01 |
| Gamma | 1.0 |
| GAE Lambda | 0.95 |
| Clip Range Policy | 0.2 |
| Clip Range Value | 0.2 |
| Whiten Advantages | true |
| Whiten Rewards | false |
| Score on EOD | true |
| Max Steps | 200 |
| PPO steps/epoch | 1 |
| Value steps/epoch | 8 |
Trajectory data config
| Hyperparameter | Value |
|---|---|
| Continuation Max Len | 1024 |
| Continuation Min Len | 0 |
| Top P | 1.0 |
| Temperature | 1.0 |
Evaluation
First evaluation results aggregated from the EleutherAI harness:
- Arithmetic capabilities become much worse
- Common Sense, Paraphrase, Reasoning, Reading Comprehension stay at about the same level
- NLI becomes better and QA gets worse
Overall these results were expected from the literature. Benchmarks don't really correlate with human preference. All these metrics use a Select methodology, and it since RLHF models are far less calibrated than raw LLMs, they will be punished in these evaluations.
Human evaluation is currently ongoing.
Compute Infrastructure
Hardware
Alfred-40B-0723 was trained on AWS SageMaker, on 128 A100 40GB GPUs in P4d instances.
Software
Alfred-40B-0723 was trained with a custom RLHF codebase. Training leverages a 3D parallelism approach combined with ZeRO, as well as high-performance kernels such as FlashAttention.