commit 0322a597ff1b77d4ae8e306feb1893d7fe4282df Author: ModelHub XC Date: Sun Jun 7 18:06:20 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: silma-ai/SILMA-9B-Instruct-v1.0 Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..d262da9 --- /dev/null +++ b/README.md @@ -0,0 +1,758 @@ +--- +license: gemma +library_name: transformers +pipeline_tag: text-generation +extra_gated_button_content: Acknowledge license +tags: +- conversational +language: +- ar +- en +model-index: + - name: SILMA-9B-Instruct-v1.0 + results: + - task: + type: text-generation + dataset: + name: Arabic Broad Benchmark (ABB) + type: silma-ai/arabic-broad-benchmark + metrics: + - name: benchmark_score + type: acc (1-10) + value: 6.15 + source: + name: Arabic Broad Leaderboard (ABL) + url: https://huggingface.co/spaces/silma-ai/Arabic-LLM-Broad-Leaderboard + + - task: + type: text-generation + dataset: + name: MMLU (Arabic) + type: OALL/Arabic_MMLU + metrics: + - name: acc_norm + type: loglikelihood_acc_norm + value: 52.55 + source: + name: Open Arabic LLM Leaderboard + url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard-v1 + - task: + type: text-generation + dataset: + name: AlGhafa + type: OALL/AlGhafa-Arabic-LLM-Benchmark-Native + metrics: + - name: acc_norm + type: loglikelihood_acc_norm + value: 71.85 + source: + name: Open Arabic LLM Leaderboard + url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard-v1 + - task: + type: text-generation + dataset: + name: ARC Challenge (Arabic) + type: OALL/AlGhafa-Arabic-LLM-Benchmark-Translated + metrics: + - name: acc_norm + type: loglikelihood_acc_norm + value: 78.19 + source: + name: Open Arabic LLM Leaderboard + url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard-v1 + - task: + type: text-generation + dataset: + name: ACVA + type: OALL/ACVA + metrics: + - name: acc_norm + type: loglikelihood_acc_norm + value: 78.89 + source: + name: Open Arabic LLM Leaderboard + url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard-v1 + - task: + type: text-generation + dataset: + name: Arabic_EXAMS + type: OALL/Arabic_EXAMS + metrics: + - name: acc_norm + type: loglikelihood_acc_norm + value: 51.4 + source: + name: Open Arabic LLM Leaderboard + url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard-v1 + - task: + type: text-generation + dataset: + name: ARC Easy + type: OALL/AlGhafa-Arabic-LLM-Benchmark-Translated + metrics: + - name: acc_norm + type: loglikelihood_acc_norm + value: 86 + source: + name: Open Arabic LLM Leaderboard + url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard-v1 + - task: + type: text-generation + dataset: + name: BOOLQ (Arabic) + type: OALL/AlGhafa-Arabic-LLM-Benchmark-Translated + metrics: + - name: acc_norm + type: loglikelihood_acc_norm + value: 64.05 + source: + name: Open Arabic LLM Leaderboard + url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard-v1 + - task: + type: text-generation + dataset: + name: COPA (Arabic) + type: OALL/AlGhafa-Arabic-LLM-Benchmark-Translated + metrics: + - name: acc_norm + type: loglikelihood_acc_norm + value: 78.89 + source: + name: Open Arabic LLM Leaderboard + url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard-v1 + - task: + type: text-generation + dataset: + name: HELLASWAG (Arabic) + type: OALL/AlGhafa-Arabic-LLM-Benchmark-Translated + metrics: + - name: acc_norm + type: loglikelihood_acc_norm + value: 47.64 + source: + name: Open Arabic LLM Leaderboard + url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard-v1 + - task: + type: text-generation + dataset: + name: OPENBOOK QA (Arabic) + type: OALL/AlGhafa-Arabic-LLM-Benchmark-Translated + metrics: + - name: acc_norm + type: loglikelihood_acc_norm + value: 72.93 + source: + name: Open Arabic LLM Leaderboard + url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard-v1 + - task: + type: text-generation + dataset: + name: PIQA (Arabic) + type: OALL/AlGhafa-Arabic-LLM-Benchmark-Translated + metrics: + - name: acc_norm + type: loglikelihood_acc_norm + value: 71.96 + source: + name: Open Arabic LLM Leaderboard + url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard-v1 + - task: + type: text-generation + dataset: + name: RACE (Arabic) + type: OALL/AlGhafa-Arabic-LLM-Benchmark-Translated + metrics: + - name: acc_norm + type: loglikelihood_acc_norm + value: 75.55 + source: + name: Open Arabic LLM Leaderboard + url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard-v1 + - task: + type: text-generation + dataset: + name: SCIQ (Arabic) + type: OALL/AlGhafa-Arabic-LLM-Benchmark-Translated + metrics: + - name: acc_norm + type: loglikelihood_acc_norm + value: 91.26 + source: + name: Open Arabic LLM Leaderboard + url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard-v1 + - task: + type: text-generation + dataset: + name: TOXIGEN (Arabic) + type: OALL/AlGhafa-Arabic-LLM-Benchmark-Translated + metrics: + - name: acc_norm + type: loglikelihood_acc_norm + value: 67.59 + source: + name: Open Arabic LLM Leaderboard + url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard-v1 + - task: + type: text-generation + name: Text Generation + dataset: + name: IFEval (0-Shot) + type: HuggingFaceH4/ifeval + args: + num_few_shot: 0 + metrics: + - type: inst_level_strict_acc and prompt_level_strict_acc + value: 58.42 + name: strict accuracy + source: + url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=silma-ai/SILMA-9B-Instruct-v1.0 + name: Open LLM Leaderboard + - task: + type: text-generation + name: Text Generation + dataset: + name: BBH (3-Shot) + type: BBH + args: + num_few_shot: 3 + metrics: + - type: acc_norm + value: 30.71 + name: normalized accuracy + source: + url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=silma-ai/SILMA-9B-Instruct-v1.0 + name: Open LLM Leaderboard + - task: + type: text-generation + name: Text Generation + dataset: + name: MATH Lvl 5 (4-Shot) + type: hendrycks/competition_math + args: + num_few_shot: 4 + metrics: + - type: exact_match + value: 0.0 + name: exact match + source: + url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=silma-ai/SILMA-9B-Instruct-v1.0 + name: Open LLM Leaderboard + - task: + type: text-generation + name: Text Generation + dataset: + name: GPQA (0-shot) + type: Idavidrein/gpqa + args: + num_few_shot: 0 + metrics: + - type: acc_norm + value: 7.38 + name: acc_norm + source: + url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=silma-ai/SILMA-9B-Instruct-v1.0 + name: Open LLM Leaderboard + - task: + type: text-generation + name: Text Generation + dataset: + name: MuSR (0-shot) + type: TAUR-Lab/MuSR + args: + num_few_shot: 0 + metrics: + - type: acc_norm + value: 17.26 + name: acc_norm + source: + url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=silma-ai/SILMA-9B-Instruct-v1.0 + name: Open LLM Leaderboard + - task: + type: text-generation + name: Text Generation + dataset: + name: MMLU-PRO (5-shot) + type: TIGER-Lab/MMLU-Pro + config: main + split: test + args: + num_few_shot: 5 + metrics: + - type: acc + value: 32.44 + name: accuracy + source: + url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=silma-ai/SILMA-9B-Instruct-v1.0 + name: Open LLM Leaderboard + + + +--- + + +# SILMA AI + +SILMA.AI is a leading Generative AI startup dedicated to empowering Arabic speakers with state-of-the-art AI solutions. + + +## 🚀 Our Flagship Model: SILMA 1.0 🚀 + +* **SILMA 1.0** was the **TOP-RANKED** open-weights Arabic LLM (Until February 2025) with an impressive **9 billion parameter size**, surpassing models that are over seven times larger 🏆 + +**Important Tip:** 💡 For RAG use-cases please use [SILMA Kashif v1.0](https://huggingface.co/silma-ai/SILMA-Kashif-2B-Instruct-v1.0) as it has been specifically trained for Question Answering tasks. + + +## What makes SILMA exceptional? + +* SIMLA is a small language model outperforming 72B models in most arabic language tasks, thus more practical for business use-cases +* SILMA is built over the robust foundational models of Google Gemma, combining the strengths of both to provide you with unparalleled performance +* SILMA is an open-weight model, free to use in accordance with our open license + + +## 👥 Our Team + +We are a team of seasoned **Arabic AI experts** who understand the nuances of the language and cultural considerations, enabling us to build solutions that truly resonate with Arabic users. + +**Authors**: [silma.ai](https://silma.ai) + + +### Usage + +Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with: + +```sh +pip install -U transformers sentencepiece +``` + +Then, copy the snippet from the section that is relevant for your usecase. + +#### Running with the `pipeline` API + +```python +import torch +from transformers import pipeline + +pipe = pipeline( + "text-generation", + model="silma-ai/SILMA-9B-Instruct-v1.0", + model_kwargs={"torch_dtype": torch.bfloat16}, + device="cuda", # replace with "mps" to run on a Mac device +) + +messages = [ + {"role": "user", "content": "اكتب رسالة تعتذر فيها لمديري في العمل عن الحضور اليوم لأسباب مرضية."}, +] + +outputs = pipe(messages, max_new_tokens=256) +assistant_response = outputs[0]["generated_text"][-1]["content"].strip() +print(assistant_response) +``` + +- Response: + +```text +السلام عليكم ورحمة الله وبركاته + +أودّ أن أعتذر عن عدم الحضور إلى العمل اليوم بسبب مرضي. أشعر بالسوء الشديد وأحتاج إلى الراحة. سأعود إلى العمل فور تعافيي. +شكراً لتفهمكم. + +مع تحياتي، +[اسمك] +``` + +#### Running the model on a single / multi GPU + +```sh +pip install accelerate +``` + +```python +from transformers import AutoTokenizer, AutoModelForCausalLM +import torch + +model_id = "silma-ai/SILMA-9B-Instruct-v1.0" +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained( + model_id, + device_map="auto", + torch_dtype=torch.bfloat16, +) + +messages = [ + {"role": "system", "content": "أنت مساعد ذكي للإجابة عن أسئلة المستخدمين."}, + {"role": "user", "content": "أيهما أبعد عن الأرض, الشمس أم القمر؟"}, +] + +input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda") + +outputs = model.generate(**input_ids, max_new_tokens=256) + +print(tokenizer.decode(outputs[0])) +``` + +- Response: +```text +الشمس +``` + +You can ensure the correct chat template is applied by using `tokenizer.apply_chat_template` as follows: +```python + +from transformers import AutoTokenizer, AutoModelForCausalLM +import torch + +model_id = "silma-ai/SILMA-9B-Instruct-v1.0" +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained( + model_id, + device_map="auto", + torch_dtype=torch.bfloat16, +) + +messages = [ + {"role": "system", "content": "أنت مساعد ذكي للإجابة عن أسئلة المستخدمين."}, + {"role": "user", "content": "اكتب كود بايثون لتوليد متسلسلة أرقام زوجية."}, +] +input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda") + +outputs = model.generate(**input_ids, max_new_tokens=256) +print(tokenizer.decode(outputs[0]).split("model")[-1]) +``` + +- Response: +```python +def generate_even_numbers(n): + """ + This function generates a list of even numbers from 1 to n. + Args: + n: The upper limit of the range. + + Returns: + A list of even numbers. + """ + return [i for i in range(1, n + 1) if i % 2 == 0] + +# Example usage +n = 10 +even_numbers = generate_even_numbers(n) +print(f"The first {n} even numbers are: {even_numbers}") +``` + +#### Quantized Versions through `bitsandbytes` + +
+ + Using 8-bit precision (int8) + + +```sh +pip install bitsandbytes accelerate +``` + +```python +# pip install bitsandbytes accelerate +from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig + +model_id = "silma-ai/SILMA-9B-Instruct-v1.0" +quantization_config = BitsAndBytesConfig(load_in_8bit=True) + +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained( + model_id, + quantization_config=quantization_config, +) + +messages = [ + {"role": "system", "content": "أنت مساعد ذكي للإجابة عن أسئلة المستخدمين."}, + {"role": "user", "content": "اذكر خمس انواع فواكه بها نسب عالية من فيتامين ج."}, +] +input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda") + +outputs = model.generate(**input_ids, max_new_tokens=256) +print(tokenizer.decode(outputs[0]).split("model")[-1]) +``` + +- Response: +```text +الليمون، البرتقال، الموز، الكيوي، الفراولة +``` + +
+ +
+ + Using 4-bit precision + + +```python +# pip install bitsandbytes accelerate +from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig + +model_id = "silma-ai/SILMA-9B-Instruct-v1.0" +quantization_config = BitsAndBytesConfig(load_in_4bit=True) + +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained( + model_id, + quantization_config=quantization_config, +) + +messages = [ + {"role": "system", "content": "أنت مساعد ذكي للإجابة عن أسئلة المستخدمين."}, + {"role": "user", "content": "في أي عام توفى صلاح الدين الأيوبي؟"}, +] +input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda") + +outputs = model.generate(**input_ids, max_new_tokens=256) +print(tokenizer.decode(outputs[0]).split("model")[-1]) +``` + +- Response: +```text +1193 +``` + +
+ +#### Advanced Usage + +
+ + Torch compile + + +[Torch compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) is a method for speeding-up the +inference of PyTorch modules. The Silma model can be run up to 6x faster by leveraging torch compile. + +Note that two warm-up steps are required before the full inference speed is realised: + +```python +import os +os.environ["TOKENIZERS_PARALLELISM"] = "false" + +from transformers import AutoTokenizer, Gemma2ForCausalLM +from transformers.cache_utils import HybridCache +import torch + +torch.set_float32_matmul_precision("high") + +# load the model + tokenizer +model_id = "silma-ai/SILMA-9B-Instruct-v1.0" +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = Gemma2ForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16) +model.to("cuda") + +# apply the torch compile transformation +model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True) + +# pre-process inputs + +messages = [ + {"role": "system", "content": "أنت مساعد ذكي للإجابة عن أسئلة المستخدمين."}, + {"role": "user", "content": "من الرئيس الذي تولى المنصب في أمريكا بعد دونالد ترامب؟"}, +] +model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda") + +input_text = "من الرئيس الذي تولى المنصب في أمريكا بعد دونالد ترامب؟" +model_inputs = tokenizer(input_text, return_tensors="pt").to("cuda") +prompt_length = model_inputs.input_ids.shape[1] + +# set-up k/v cache +past_key_values = HybridCache( + config=model.config, + max_batch_size=1, + max_cache_len=model.config.max_position_embeddings, + device=model.device, + dtype=model.dtype +) + +# enable passing kv cache to generate +model._supports_cache_class = True +model.generation_config.cache_implementation = None + +# two warm-up steps +for idx in range(2): + outputs = model.generate(**model_inputs, past_key_values=past_key_values, do_sample=True, temperature=1.0, max_new_tokens=128) + past_key_values.reset() + +# fast run +outputs = model.generate(**model_inputs, past_key_values=past_key_values, do_sample=True, temperature=1.0, max_new_tokens=128) +print(tokenizer.decode(outputs[0], skip_special_tokens=True)) +``` + +- Response: +```text +جو بايدن +``` + +For more details, refer to the [Transformers documentation](https://huggingface.co/docs/transformers/main/en/llm_optims?static-kv=basic+usage%3A+generation_config). + +
+ +### Chat Template + +The instruction-tuned models use a chat template that must be adhered to for conversational use. +The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet. + +Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction: + +```python +from transformers import AutoTokenizer, AutoModelForCausalLM +import transformers +import torch + +model_id = "silma-ai/SILMA-9B-Instruct-v1.0" +dtype = torch.bfloat16 + +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained( + model_id, + device_map="cuda", + torch_dtype=dtype,) + +chat = [ + { "role": "user", "content": "ما اشهر اطارات العمل في البايثون لبناء نماذج الذكاء الاصطناعي؟" }, +] +prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) +``` + +At this point, the prompt contains the following text: + +``` +user +ما اشهر اطارات العمل في البايثون لبناء نماذج الذكاء الاصطناعي؟ +model +``` + +As you can see, each turn is preceded by a `` delimiter and then the role of the entity +(either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with +the `` token. + +You can follow this format to build the prompt manually, if you need to do it without the tokenizer's +chat template. + +After the prompt is ready, generation can be performed like this: + +```python +inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") +outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150) +print(tokenizer.decode(outputs[0])) +``` + +### Inputs and outputs + +* **Input:** Text string, such as a question, a prompt, or a document to be + summarized. +* **Output:** Generated Arabic or English text in response to the input, such + as an answer to a question, or a summary of a document. + + +### GPU Requirements + +The following are the minimum/recommended GPU requirements for running inference: + +* Recommended + * At least one GPU with a minimum of 48 GB of GPU memory + * Examples: Nvidia A40, L40, RTX A6000 + +* Minimum + + * At least one GPU with 16-24 GB of GPU memory + * Examples: Nvidia RTX 4090, RTX 4000, L4 + * Assuming that the model is loaded in either 8-bit or 4-bit [Quantization mode](https://huggingface.co/silma-ai/SILMA-9B-Instruct-v1.0#quantized-versions-through-bitsandbytes) + + +### Citation + +```bibtex +@misc{silma-9b-2024, + author = {{silma-ai}}, + title = {SILMA 9B Instruct v1.0}, + year = {2024}, + howpublished = {\url{https://huggingface.co/silma-ai/SILMA-9B-Instruct-v1.0}} +} +``` + +## Usage and Limitations + +These models have certain limitations that users should be aware of. + +### Intended Usage + +Open Large Language Models (LLMs) have a wide range of applications across +various industries and domains. The following list of potential uses is not +comprehensive. The purpose of this list is to provide contextual information +about the possible use-cases that the model creators considered as part of model +training and development. + +* Content Creation and Communication + * Text Generation: These models can be used to generate creative text formats + such as poems, scripts, code, marketing copy, and email drafts. + * Chatbots and Conversational AI: Power conversational interfaces for customer + service, virtual assistants, or interactive applications. + * Text Summarization: Generate concise summaries of a text corpus, research + papers, or reports. +* Research and Education + * Natural Language Processing (NLP) Research: These models can serve as a + foundation for researchers to experiment with NLP techniques, develop + algorithms, and contribute to the advancement of the field. + * Language Learning Tools: Support interactive language learning experiences, + aiding in grammar correction or providing writing practice. + * Knowledge Exploration: Assist researchers in exploring large bodies of text + by generating summaries or answering questions about specific topics. + +### Limitations + +* Training Data + * The quality and diversity of the training data significantly influence the + model's capabilities. Biases or gaps in the training data can lead to + limitations in the model's responses. + * The scope of the training dataset determines the subject areas the model can + handle effectively. +* Context and Task Complexity + * LLMs are better at tasks that can be framed with clear prompts and + instructions. Open-ended or highly complex tasks might be challenging. + * A model's performance can be influenced by the amount of context provided + (longer context generally leads to better outputs, up to a certain point). +* Language Ambiguity and Nuance + * Natural language is inherently complex. LLMs might struggle to grasp subtle + nuances, sarcasm, or figurative language. +* Factual Accuracy + * LLMs generate responses based on information they learned from their + training datasets, but they are not knowledge bases. They may generate + incorrect or outdated factual statements. +* Common Sense + * LLMs rely on statistical patterns in language. They might lack the ability + to apply common sense reasoning in certain situations. + +### Ethical Considerations and Risks + +The development of large language models (LLMs) raises several ethical concerns. +In creating an open model, we have carefully considered the following: + +* Bias and Fairness + * LLMs trained on large-scale, real-world text data can reflect socio-cultural + biases embedded in the training material. +* Misinformation and Misuse + * LLMs can be misused to generate text that is false, misleading, or harmful. + * Guidelines are provided for responsible use with the model, see the + [Responsible Generative AI Toolkit][rai-toolkit]. +* Transparency and Accountability: + * This model card summarizes details on the models' architecture, + capabilities, limitations, and evaluation processes. + * A responsibly developed open model offers the opportunity to share + innovation by making LLM technology accessible to developers and researchers + across the AI ecosystem. + +Risks identified and mitigations: + +* Perpetuation of biases: It's encouraged to perform continuous monitoring + (using evaluation metrics, human review) and the exploration of de-biasing + techniques during model training, fine-tuning, and other use cases. +* Generation of harmful content: Mechanisms and guidelines for content safety + are essential. Developers are encouraged to exercise caution and implement + appropriate content safety safeguards based on their specific product policies + and application use cases. +* Privacy violations: Models were trained on data filtered for removal of PII + (Personally Identifiable Information). Developers are encouraged to adhere to + privacy regulations with privacy-preserving techniques. \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..4eacb71 --- /dev/null +++ b/config.json @@ -0,0 +1,34 @@ +{ + "_name_or_path": "google/gemma-2-9b-it", + "architectures": [ + "Gemma2ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "attn_logit_softcapping": 50.0, + "bos_token_id": 2, + "cache_implementation": "hybrid", + "eos_token_id": 1, + "final_logit_softcapping": 30.0, + "head_dim": 256, + "hidden_act": "gelu_pytorch_tanh", + "hidden_activation": "gelu_pytorch_tanh", + "hidden_size": 3584, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 8192, + "model_type": "gemma2", + "num_attention_heads": 16, + "num_hidden_layers": 42, + "num_key_value_heads": 8, + "pad_token_id": 0, + "query_pre_attn_scalar": 256, + "rms_norm_eps": 1e-06, + "rope_theta": 10000.0, + 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