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gpt4all-falcon - GGUF
- Model creator: https://huggingface.co/nomic-ai/
- Original model: https://huggingface.co/nomic-ai/gpt4all-falcon/
| Name | Quant method | Size |
|---|---|---|
| gpt4all-falcon.Q2_K.gguf | Q2_K | 3.59GB |
| gpt4all-falcon.IQ3_XS.gguf | IQ3_XS | 3.59GB |
| gpt4all-falcon.IQ3_S.gguf | IQ3_S | 3.59GB |
| gpt4all-falcon.Q3_K_S.gguf | Q3_K_S | 3.59GB |
| gpt4all-falcon.IQ3_M.gguf | IQ3_M | 3.71GB |
| gpt4all-falcon.Q3_K.gguf | Q3_K | 3.86GB |
| gpt4all-falcon.Q3_K_M.gguf | Q3_K_M | 3.86GB |
| gpt4all-falcon.Q3_K_L.gguf | Q3_K_L | 4.08GB |
| gpt4all-falcon.IQ4_XS.gguf | IQ4_XS | 3.89GB |
| gpt4all-falcon.Q4_0.gguf | Q4_0 | 3.92GB |
| gpt4all-falcon.IQ4_NL.gguf | IQ4_NL | 3.96GB |
| gpt4all-falcon.Q4_K_S.gguf | Q4_K_S | 4.42GB |
| gpt4all-falcon.Q4_K.gguf | Q4_K | 4.63GB |
| gpt4all-falcon.Q4_K_M.gguf | Q4_K_M | 4.63GB |
| gpt4all-falcon.Q4_1.gguf | Q4_1 | 4.32GB |
| gpt4all-falcon.Q5_0.gguf | Q5_0 | 4.73GB |
| gpt4all-falcon.Q5_K_S.gguf | Q5_K_S | 4.98GB |
| gpt4all-falcon.Q5_K.gguf | Q5_K | 5.34GB |
| gpt4all-falcon.Q5_K_M.gguf | Q5_K_M | 5.34GB |
| gpt4all-falcon.Q5_1.gguf | Q5_1 | 5.13GB |
| gpt4all-falcon.Q6_K.gguf | Q6_K | 6.55GB |
Original model description:
license: apache-2.0 datasets: - nomic-ai/gpt4all-j-prompt-generations language: - en pipeline_tag: text-generation
Model Card for GPT4All-Falcon
An Apache-2 licensed chatbot trained over a massive curated corpus of assistant interactions including word problems, multi-turn dialogue, code, poems, songs, and stories.
Model Details
Model Description
This model has been finetuned from Falcon
- Developed by: Nomic AI
- Model Type: A finetuned Falcon 7B model on assistant style interaction data
- Language(s) (NLP): English
- License: Apache-2
- Finetuned from model [optional]: Falcon
To download a model with a specific revision run
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("nomic-ai/gpt4all-falcon", trust_remote_code=True)
Downloading without specifying revision defaults to main/v1.0.
To use it for inference with Cuda, run
from transformers import AutoTokenizer, pipeline
import transformers
import torch
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model.to("cuda:0")
prompt = "Describe a painting of a falcon in a very detailed way." # Change this to your prompt
prompt_template = f"### Instruction: {prompt}\n### Response:"
tokens = tokenizer(prompt_template, return_tensors="pt").input_ids.to("cuda:0")
output = model.generate(input_ids=tokens, max_new_tokens=256, do_sample=True, temperature=0.8)
# Print the generated text
print(tokenizer.decode(output[0]))
Model Sources [optional]
- Repository: https://github.com/nomic-ai/gpt4all
- Base Model Repository: https://huggingface.co/tiiuae/falcon-7b
- Demo [optional]: https://gpt4all.io/
Training Procedure
GPT4All is made possible by our compute partner Paperspace.
Trained on a DGX cluster with 8 A100 80GB GPUs for ~12 hours. Using Deepspeed + Accelerate, we use a global batch size of 256 with a learning rate of 2e-5. More information can be found in the repo.
Results
Results on common sense reasoning benchmarks
| Model | BoolQ | PIQA | HellaSwag | WinoGrande | ARC-e | ARC-c | OBQA | Avg. |
|---|---|---|---|---|---|---|---|---|
| GPT4All-J 6B v1.0 | 73.4 | 74.8 | 63.4 | 64.7 | 54.9 | 36.0 | 40.2 | 58.2 |
| GPT4All-J v1.1-breezy | 74.0 | 75.1 | 63.2 | 63.6 | 55.4 | 34.9 | 38.4 | 57.8 |
| GPT4All-J v1.2-jazzy | 74.8 | 74.9 | 63.6 | 63.8 | 56.6 | 35.3 | 41.0 | 58.6 |
| GPT4All-J v1.3-groovy | 73.6 | 74.3 | 63.8 | 63.5 | 57.7 | 35.0 | 38.8 | 58.1 |
| GPT4All-J Lora 6B | 68.6 | 75.8 | 66.2 | 63.5 | 56.4 | 35.7 | 40.2 | 58.1 |
| GPT4All LLaMa Lora 7B | 73.1 | 77.6 | 72.1 | 67.8 | 51.1 | 40.4 | 40.2 | 60.3 |
| GPT4All 13B snoozy | 83.3 | 79.2 | 75.0 | 71.3 | 60.9 | 44.2 | 43.4 | 65.3 |
| GPT4All Falcon | 77.6 | 79.8 | 74.9 | 70.1 | 67.9 | 43.4 | 42.6 | 65.2 |
| Dolly 6B | 68.8 | 77.3 | 67.6 | 63.9 | 62.9 | 38.7 | 41.2 | 60.1 |
| Dolly 12B | 56.7 | 75.4 | 71.0 | 62.2 | 64.6 | 38.5 | 40.4 | 58.4 |
| Alpaca 7B | 73.9 | 77.2 | 73.9 | 66.1 | 59.8 | 43.3 | 43.4 | 62.4 |
| Alpaca Lora 7B | 74.3 | 79.3 | 74.0 | 68.8 | 56.6 | 43.9 | 42.6 | 62.8 |
| GPT-J 6.7B | 65.4 | 76.2 | 66.2 | 64.1 | 62.2 | 36.6 | 38.2 | 58.4 |
| LLama 7B | 73.1 | 77.4 | 73.0 | 66.9 | 52.5 | 41.4 | 42.4 | 61.0 |
| LLama 13B | 68.5 | 79.1 | 76.2 | 70.1 | 60.0 | 44.6 | 42.2 | 63.0 |
| Pythia 6.7B | 63.5 | 76.3 | 64.0 | 61.1 | 61.3 | 35.2 | 37.2 | 57.0 |
| Pythia 12B | 67.7 | 76.6 | 67.3 | 63.8 | 63.9 | 34.8 | 38 | 58.9 |
| Fastchat T5 | 81.5 | 64.6 | 46.3 | 61.8 | 49.3 | 33.3 | 39.4 | 53.7 |
| Fastchat Vicuña 7B | 76.6 | 77.2 | 70.7 | 67.3 | 53.5 | 41.2 | 40.8 | 61.0 |
| Fastchat Vicuña 13B | 81.5 | 76.8 | 73.3 | 66.7 | 57.4 | 42.7 | 43.6 | 63.1 |
| StableVicuña RLHF | 82.3 | 78.6 | 74.1 | 70.9 | 61.0 | 43.5 | 44.4 | 65.0 |
| StableLM Tuned | 62.5 | 71.2 | 53.6 | 54.8 | 52.4 | 31.1 | 33.4 | 51.3 |
| StableLM Base | 60.1 | 67.4 | 41.2 | 50.1 | 44.9 | 27.0 | 32.0 | 42.2 |
| Koala 13B | 76.5 | 77.9 | 72.6 | 68.8 | 54.3 | 41.0 | 42.8 | 62.0 |
| Open Assistant Pythia 12B | 67.9 | 78.0 | 68.1 | 65.0 | 64.2 | 40.4 | 43.2 | 61.0 |
| Mosaic MPT7B | 74.8 | 79.3 | 76.3 | 68.6 | 70.0 | 42.2 | 42.6 | 64.8 |
| Mosaic mpt-instruct | 74.3 | 80.4 | 77.2 | 67.8 | 72.2 | 44.6 | 43.0 | 65.6 |
| Mosaic mpt-chat | 77.1 | 78.2 | 74.5 | 67.5 | 69.4 | 43.3 | 44.2 | 64.9 |
| Wizard 7B | 78.4 | 77.2 | 69.9 | 66.5 | 56.8 | 40.5 | 42.6 | 61.7 |
| Wizard 7B Uncensored | 77.7 | 74.2 | 68.0 | 65.2 | 53.5 | 38.7 | 41.6 | 59.8 |
| Wizard 13B Uncensored | 78.4 | 75.5 | 72.1 | 69.5 | 57.5 | 40.4 | 44.0 | 62.5 |
| GPT4-x-Vicuna-13b | 81.3 | 75.0 | 75.2 | 65.0 | 58.7 | 43.9 | 43.6 | 62.2 |
| Falcon 7b | 73.6 | 80.7 | 76.3 | 67.3 | 71.0 | 43.3 | 44.4 | 65.2 |
| text-davinci-003 | 88.1 | 83.8 | 83.4 | 75.8 | 83.9 | 63.9 | 51.0 | 75.7 |
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