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Model: stabilityai/stablelm-2-zephyr-1_6b
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LICENSE

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+STABILITY AI NON-COMMERCIAL RESEARCH COMMUNITY LICENSE AGREEMENT	
+Dated: December 06, 2023
+
+By using or distributing any portion or element of the Models, Software, Software Products or Derivative Works, you agree to be bound by this Agreement.
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+principles. 

README.md

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+---
+datasets:
+- HuggingFaceH4/ultrachat_200k
+- allenai/ultrafeedback_binarized_cleaned
+- meta-math/MetaMathQA
+- WizardLM/WizardLM_evol_instruct_V2_196k
+- openchat/openchat_sharegpt4_dataset
+- LDJnr/Capybara
+- Intel/orca_dpo_pairs
+- hkust-nlp/deita-10k-v0
+language:
+- en
+tags:
+- causal-lm
+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
+license: other
+---
+# `StableLM 2 Zephyr 1.6B`
+
+## Model Description
+
+`Stable LM 2 Zephyr 1.6B` is a 1.6 billion parameter instruction tuned language model inspired by [HugginFaceH4's Zephyr 7B](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) training pipeline. The model is trained on a mix of publicly available datasets and synthetic datasets, utilizing [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290).
+
+## Usage
+
+`StableLM 2 Zephyr 1.6B` uses the following instruction format:
+```
+<|user|>
+Which famous math number begins with 1.6 ...?<|endoftext|>
+<|assistant|>
+The number you are referring to is 1.618033988749895. This is the famous value known as the golden ratio<|endoftext|>
+```
+
+This format is also available through the tokenizer's `apply_chat_template` method:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained('stabilityai/stablelm-2-zephyr-1_6b')
+model = AutoModelForCausalLM.from_pretrained(
+    'stabilityai/stablelm-2-zephyr-1_6b',
+    device_map="auto"
+)
+
+prompt = [{'role': 'user', 'content': 'Which famous math number begins with 1.6 ...?'}]
+inputs = tokenizer.apply_chat_template(
+    prompt,
+    add_generation_prompt=True,
+    return_tensors='pt'
+)
+
+tokens = model.generate(
+    inputs.to(model.device),
+    max_new_tokens=1024,
+    temperature=0.5,
+    do_sample=True
+)
+
+print(tokenizer.decode(tokens[0], skip_special_tokens=False))
+```
+
+## Model Details
+
+* **Developed by**: [Stability AI](https://stability.ai/)
+* **Model type**: `StableLM 2 Zephyr 1.6B` model is an auto-regressive language model based on the transformer decoder architecture.
+* **Language(s)**: English
+* **Paper**: [Stable LM 2 1.6B Technical Report](https://drive.google.com/file/d/1JYJHszhS8EFChTbNAf8xmqhKjogWRrQF/view?usp=sharing)
+* **Library**: [Alignment Handbook](https://github.com/huggingface/alignment-handbook.git)
+* **Finetuned from model**: [https://huggingface.co/stabilityai/stablelm-2-1_6b](https://huggingface.co/stabilityai/stablelm-2-1_6b)
+* **License**: [StabilityAI Non-Commercial Research Community License](https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b/blob/main/LICENSE). If you want to use this model for your commercial products or purposes, please contact us [here](https://stability.ai/contact) to learn more.
+* **Contact**: For questions and comments about the model, please email `lm@stability.ai`
+
+### Training Dataset
+
+The dataset is comprised of a mixture of open datasets large-scale datasets available on the [HuggingFace Hub](https://huggingface.co/datasets):
+1. SFT Datasets
+- HuggingFaceH4/ultrachat_200k
+- meta-math/MetaMathQA
+- WizardLM/WizardLM_evol_instruct_V2_196k
+- Open-Orca/SlimOrca
+- openchat/openchat_sharegpt4_dataset
+- LDJnr/Capybara
+- hkust-nlp/deita-10k-v0
+
+2. Preference Datasets:
+- allenai/ultrafeedback_binarized_cleaned
+- Intel/orca_dpo_pairs
+
+## Performance
+
+### MT-Bench
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/61b2bf4f5b1f7cad1799cfbb/QH00HVM3lg-5f17U_py4K.png" alt="mt_bench_plot" width="600"/>
+
+| 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-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     |
+
+### OpenLLM Leaderboard
+
+| 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-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%       |
+
+
+
+### Training Infrastructure
+
+* **Hardware**: `StableLM 2 Zephyr 1.6B` was trained on the Stability AI cluster across 8 nodes with 8 A100 80GBs GPUs for each nodes.
+* **Code Base**: We use our internal script for SFT steps and used [HuggingFace Alignment Handbook script](https://github.com/huggingface/alignment-handbook) for DPO training.
+
+## Use and Limitations
+
+### Intended Use
+
+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](#limitations-and-bias) below.
+
+### Limitations and Bias
+​
+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.
+
+
+## How to Cite
+
+```bibtex
+@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}
+}
+```

config.json

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+{
+  "architectures": [
+    "StableLmForCausalLM"
+  ],
+  "bos_token_id": 100257,
+  "eos_token_id": 100257,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 5632,
+  "max_position_embeddings": 4096,
+  "model_type": "stablelm",
+  "layer_norm_eps": 1e-05,
+  "num_attention_heads": 32,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 32,
+  "partial_rotary_factor": 0.25,
+  "rope_theta": 10000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.38.0",
+  "use_cache": true,
+  "use_qkv_bias": true,
+  "vocab_size": 100352
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

configuration_stablelm.py

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+# coding=utf-8
+# Copyright 2024 Stability AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" StableLM model configuration """
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+STABLELM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "stabilityai/stablelm-3b-4e1t": "https://huggingface.co/stabilityai/stablelm-3b-4e1t/resolve/main/config.json",
+    # See all StableLM models at https://huggingface.co/models?filter=stablelm
+}
+
+
+class StableLmConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`~StableLmModel`].
+    It is used to instantiate an StableLM model according to the specified arguments, defining the model
+    architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
+    the StableLM [stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t) architecture.
+
+    Configuration objects inherit from  [`PretrainedConfig`] and can be used
+    to control the model outputs. Read the documentation from  [`PretrainedConfig`]
+    for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 50304):
+            Vocabulary size of the StableLM model. Defines the number of different tokens that
+            can be represented by the `inputs_ids` passed when calling [`StableLmModel`].
+        intermediate_size (`int`, *optional*, defaults to 6912):
+            Dimension of the MLP representations.
+        hidden_size (`int`, *optional*, defaults to 2560):
+            Number of hidden layers in the Transformer decoder.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 32):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string).
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model might ever be used with.
+            Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing
+             all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions
+            (not used by all models). Only relevant if `config.is_decoder=True`.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to `10000.0`):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
+            is an experimental feature, subject to breaking API changes in future versions.
+        use_qkv_bias (`bool`, *optional*, defaults to `False`):
+            Whether or not the model should use bias for qkv layers.
+        hidden_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio after applying the MLP to the hidden states.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        partial_rotary_factor (`float`, *optional*, defaults to 0.25):
+            Percentage of the query and keys which will have rotary embedding.
+        bos_token_id (int, *optional*, defaults to 0):
+            The id of the `BOS` token in the vocabulary.
+        eos_token_id (int, *optional*, defaults to 0):
+            The id of the `EOS` token in the vocabulary.
+
+    Example:
+
+    ```python
+    >>> from transformers import StableLmModel, StableLmConfig
+
+    >>> # Initializing a StableLM stablelm-3b style configuration
+    >>> configuration = StableLmConfig()
+    ```"""
+
+    model_type = "stablelm"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=50304,
+        intermediate_size=6912,
+        hidden_size=2560,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        layer_norm_eps=1.0e-5,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10_000,
+        rope_scaling=None,
+        use_qkv_bias=False,
+        hidden_dropout=0.0,
+        attention_dropout=0.0,
+        partial_rotary_factor=0.25,
+        bos_token_id=0,
+        eos_token_id=0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.use_qkv_bias = use_qkv_bias
+        self.hidden_dropout = hidden_dropout
+        self.attention_dropout = attention_dropout
+        self.partial_rotary_factor = partial_rotary_factor
+        self._rope_scaling_validation()
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 100257,
+  "eos_token_id": 100257,
+  "transformers_version": "4.38.0"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:233815ace7c2e3ecc94e9575aed9f1c5dc6935d126f6c13a7586ad28ae25ddc6
+size 3289069184

special_tokens_map.json

@@ -0,0 +1,40 @@
+{
+  "additional_special_tokens": [
+    "<|reg_extra|>",
+    "<|endoftext|>",
+    "<|fim_prefix|>",
+    "<|fim_middle|>",
+    "<|fim_suffix|>",
+    "<|fim_pad|>",
+    "<gh_stars>",
+    "<filename>",
+    "<issue_start>",
+    "<issue_comment>",
+    "<issue_closed>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<empty_output>",
+    "<commit_before>",
+    "<commit_msg>",
+    "<commit_after>",
+    "<reponame>",
+    "<|endofprompt|>",
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|pause|>",
+    "<|reg0|>",
+    "<|reg1|>",
+    "<|reg2|>",
+    "<|reg3|>",
+    "<|reg4|>",
+    "<|reg5|>",
+    "<|reg6|>",
+    "<|reg7|>",
+    "<|extra0|>"
+  ],
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "unk_token": "<|endoftext|>"
+}

stablelm-2-zephyr-1_6b-OpenVINO-4bit.bin

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+size 1048432977

stablelm-2-zephyr-1_6b-Q4_0.gguf

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+size 982781952

stablelm-2-zephyr-1_6b-Q4_1.gguf

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+oid sha256:5869cc65f65e361979a1e0e67353354e1a5088c4e0f61a3789bc9bf4f0b435b5
+size 1072697344

stablelm-2-zephyr-1_6b-Q5_K_M.gguf

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+oid sha256:6c66d5136b2ec07d52d120717b5aa7f4c9177a9b33cfcc110ca7bf7aa5d95d10
+size 1187680256

stablelm-2-zephyr-1_6b-Q8_0.gguf

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+size 1751879680

stablelm-2-zephyr-1_6b.gguf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:852f1734d003afd6a5ac099d3a2c3b3a673e434ffcbb5e695ff4c45b5b8e18b8
+size 3293286336

tokenizer_config.json

@@ -0,0 +1,46 @@
+{
+  "add_prefix_space": false,
+  "additional_special_tokens": [
+    "<|reg_extra|>",
+    "<|endoftext|>",
+    "<|fim_prefix|>",
+    "<|fim_middle|>",
+    "<|fim_suffix|>",
+    "<|fim_pad|>",
+    "<gh_stars>",
+    "<filename>",
+    "<issue_start>",
+    "<issue_comment>",
+    "<issue_closed>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<empty_output>",
+    "<commit_before>",
+    "<commit_msg>",
+    "<commit_after>",
+    "<reponame>",
+    "<|endofprompt|>",
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|pause|>",
+    "<|reg0|>",
+    "<|reg1|>",
+    "<|reg2|>",
+    "<|reg3|>",
+    "<|reg4|>",
+    "<|reg5|>",
+    "<|reg6|>",
+    "<|reg7|>",
+    "<|extra0|>"
+  ],
+  "bos_token": "<|endoftext|>",
+  "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2TokenizerFast",
+  "model_max_length": 4096,
+  "pad_token": "<|endoftext|>",
+  "unk_token": "<|endoftext|>"
+}