README.md

---
base_model: stabilityai/stablelm-zephyr-3b
datasets:
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized
- meta-math/MetaMathQA
- WizardLM/WizardLM_evol_instruct_V2_196k
- Intel/orca_dpo_pairs
language:
- en
license: other
tags:
- causal-lm
- openvino
- nncf
- 4-bit
extra_gated_fields:
  Name: text
  Email: text
  Country: text
  Organization or Affiliation: text
  I ALLOW Stability AI to email me about new model releases: checkbox
model-index:
- name: stablelm-zephyr-3b
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 46.08
      name: normalized accuracy
    source:
      url: >-
        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-zephyr-3b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 74.16
      name: normalized accuracy
    source:
      url: >-
        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-zephyr-3b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 46.17
      name: accuracy
    source:
      url: >-
        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-zephyr-3b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 46.49
    source:
      url: >-
        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-zephyr-3b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 65.51
      name: accuracy
    source:
      url: >-
        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-zephyr-3b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 42.15
      name: accuracy
    source:
      url: >-
        https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-zephyr-3b
      name: Open LLM Leaderboard
---

This model is a quantized version of [`stabilityai/stablelm-zephyr-3b`](https://huggingface.co/stabilityai/stablelm-zephyr-3b) and is converted to the OpenVINO format. This model was obtained via the [nncf-quantization](https://huggingface.co/spaces/echarlaix/nncf-quantization) space with [optimum-intel](https://github.com/huggingface/optimum-intel).

Please note: For commercial use, please refer to https://stability.ai/license.

### Model Description
StableLM Zephyr 3B is a 3 billion parameter instruction tuned inspired by [HugginFaceH4's Zephyr 7B](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) training pipeline this model was trained on a mix of publicly available datasets, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290), evaluation for this model based on [MT Bench](https://arxiv.org/abs/2306.05685) and [Alpaca Benchmark](https://tatsu-lab.github.io/alpaca_eval/)


### Model Parameters
```
context window   = 4096
model type       = 3B
model params     = 2.80 B
BOS token        = 0 '<|endoftext|>'
EOS token        = 0 '<|endoftext|>'
UNK token        = 0 '<|endoftext|>'
PAD token        = 0 '<|endoftext|>'
```
The tokenizer of this model supports `chat_templates`



### Usage
StableLM Zephyr 3B uses the following instruction format:

```
<|user|>
List 3 synonyms for the word "tiny"<|endoftext|>
<|assistant|>
1. Dwarf
2. Little
3. Petite<|endoftext|>
```

### Model Details
- Developed by: Stability AI
- Model type: StableLM Zephyr 3B model is an auto-regressive language model based on the transformer decoder architecture.
- Language(s): English
- Library: [Alignment Handbook](https://github.com/huggingface/alignment-handbook.git)
- Finetuned from model: [stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t)
- License: [StabilityAI Community License](https://huggingface.co/stabilityai/stablelm-zephyr-3b/raw/main/LICENSE.md).
- Commercial License: to use this model commercially, please refer to https://stability.ai/license
- Contact: For questions and comments about the model, please email [email protected]


First make sure you have `optimum-intel` installed:

```bash
pip install openvino-genai==2024.4.0
pip install optimum-intel[openvino]
```

To load your model you can do as follows:

```python
from optimum.intel import OVModelForCausalLM
from transformers import AutoTokenizer, AutoConfig
from threading import Thread
from transformers import TextIteratorStreamer

model_id = "FM-1976/stablelm-zephyr-3b-openvino-4bit"
model = OVModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
ov_model = OVModelForCausalLM.from_pretrained(
    model_id = model_id,
    device='CPU',
    ov_config={"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""},
    config=AutoConfig.from_pretrained(model_id)
)


# Generation with a prompt message
question = 'Explain the plot of Cinderella in a sentence.'
messages = [
    {"role": "user", "content": question}
]

print('Question:', question)
#Credit to https://github.com/openvino-dev-samples/chatglm3.openvino/blob/main/chat.py
streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
model_inputs = tokenizer.apply_chat_template(messages,
                                                     add_generation_prompt=True,
                                                     tokenize=True,
                                                     pad_token_id=tokenizer.eos_token_id,
                                                     num_return_sequences=1,
                                                     return_tensors="pt")
generate_kwargs = dict(input_ids=model_inputs,
                        max_new_tokens=450,
                        temperature=0.1,
                        do_sample=True,
                        top_p=0.5,
                        repetition_penalty=1.178,
                        streamer=streamer)
t1 = Thread(target=ov_model.generate, kwargs=generate_kwargs)
t1.start()
for new_text in streamer:
    new_text = new_text
    print(new_text, end="", flush=True)
```