Downloads · 30 days
4
10% of all-time downloads
RichardErkhov/stabilityai_-_stablelm-2-1_6b-chat-4bits
stabilityai_-_stablelm-2-1_6b-chat-4bits is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
4
10% of all-time downloads
All-time downloads
39
Public
Parameters
1.7B
1.5 GB on disk
Likes
0
Public
Click a slice to open those files.
.safetensors1.5 GB · 100%
How the weights are stored.
U81.2B · 74%
From the Hugging Face model README
Quantization made by Richard Erkhov.
stablelm-2-1_6b-chat - bnb 4bits
datasets:
language:
StableLM 2 Chat 1.6BStable LM 2 Chat 1.6B is a 1.6 billion parameter instruction tuned language model inspired by HugginFaceH4's Zephyr 7B training pipeline. The model is trained on a mix of publicly available datasets and synthetic datasets, utilizing Direct Preference Optimization (DPO).
StableLM 2 1.6B Chat uses the following ChatML format:
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('stabilityai/stablelm-2-1_6b-chat')
model = AutoModelForCausalLM.from_pretrained(
'stabilityai/stablelm-2-1_6b-chat',
device_map="auto",
)
prompt = [{'role': 'user', 'content': 'Implement snake game using pygame'}]
inputs = tokenizer.apply_chat_template(
prompt,
add_generation_prompt=True,
return_tensors='pt'
)
tokens = model.generate(
inputs.to(model.device),
max_new_tokens=100,
temperature=0.7,
do_sample=True
)
output = tokenizer.decode(tokens[:, inputs.shape[-1]:][0], skip_special_tokens=False)
print(output)
StableLM 2 Chat 1.6B model is an auto-regressive language model based on the transformer decoder architecture.[email protected]The dataset is comprised of a mixture of open datasets large-scale datasets available on the HuggingFace Hub:
| Model | Size | MT-Bench |
|---|---|---|
| Mistral-7B-Instruct-v0.2 | 7B | 7.61 |
| Llama2-Chat | 70B | 6.86 |
| stablelm-zephyr-3b | 3B | 6.64 |
| MPT-30B-Chat | 30B | 6.39 |
| stablelm-2-1_6b-chat | 1.6B | 5.83 |
| stablelm-2-zephyr-1.6b | 1.6B | 5.42 |
| Falcon-40B-Instruct | 40B | 5.17 |
| Qwen-1.8B-Chat | 1.8B | 4.95 |
| dolphin-2.6-phi-2 | 2.7B | 4.93 |
| phi-2 | 2.7B | 4.29 |
| TinyLlama-1.1B-Chat-v1.0 | 1.1B | 3.46 |
| Model | Size | Average | ARC Challenge (acc_norm) | HellaSwag (acc_norm) | MMLU (acc_norm) | TruthfulQA (mc2) | Winogrande (acc) | Gsm8k (acc) |
|---|---|---|---|---|---|---|---|---|
| microsoft/phi-2 | 2.7B | 61.32% | 61.09% | 75.11% | 58.11% | 44.47% | 74.35% | 54.81% |
| stabilityai/stablelm-2-1_6b-chat | 1.6B | 50.80% | 43.94% | 69.22% | 41.59% | 46.52% | 64.56% | 38.96% |
| stabilityai/stablelm-2-zephyr-1_6b | 1.6B | 49.89% | 43.69% | 69.34% | 41.85% | 45.21% | 64.09% | 35.18% |
| microsoft/phi-1_5 | 1.3B | 47.69% | 52.90% | 63.79% | 43.89% | 40.89% | 72.22% | 12.43% |
| stabilityai/stablelm-2-1_6b | 1.6B | 45.54% | 43.43% | 70.49% | 38.93% | 36.65% | 65.90% | 17.82% |
| mosaicml/mpt-7b | 7B | 44.28% | 47.70% | 77.57% | 30.80% | 33.40% | 72.14% | 4.02% |
| KnutJaegersberg/Qwen-1_8B-Llamaified* | 1.8B | 44.75% | 37.71% | 58.87% | 46.37% | 39.41% | 61.72% | 24.41% |
| openlm-research/open_llama_3b_v2 | 3B | 40.28% | 40.27% | 71.60% | 27.12% | 34.78% | 67.01% | 0.91% |
| iiuae/falcon-rw-1b | 1B | 37.07% | 35.07% | 63.56% | 25.28% | 35.96% | 62.04% | 0.53% |
| TinyLlama/TinyLlama-1.1B-3T | 1.1B | 36.40% | 33.79% | 60.31% | 26.04% | 37.32% | 59.51% | 1.44% |
The model is intended to be used in chat-like applications. Developers must evaluate the model for safety performance in their specific use case. Read more about safety and limitations below.
This model is not trained against adversarial inputs. We strongly recommend pairing this model with an input and output classifier to prevent harmful responses.
Through our internal red teaming, we discovered that while the model will not output harmful information if not prompted to do so, it will hallucinate many facts. It is also willing to output potentially harmful outputs or misinformation when the user requests it. Using this model will require guardrails around your inputs and outputs to ensure that any outputs returned are not misinformation or harmful. Additionally, as each use case is unique, we recommend running your own suite of tests to ensure proper performance of this model. Finally, do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others.
@misc{StableLM-2-1.6B,
url={[https://huggingface.co/stabilityai/stablelm-2-1.6b](https://huggingface.co/stabilityai/stablelm-2-1.6b)},
title={Stable LM 2 1.6B},
author={Stability AI Language Team}
}