commit adbda30c26e99df3a84d1b0a45c5bacc19c62c87 Author: ModelHub XC Date: Mon Jun 29 09:18:15 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: CobraMamba/mamba-gpt-3b Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..c7d9f33 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,34 @@ +*.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 +*.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 diff --git a/README.md b/README.md new file mode 100644 index 0000000..1c1b08a --- /dev/null +++ b/README.md @@ -0,0 +1,230 @@ +--- +language: +- en +library_name: transformers +tags: +- gpt +- llm +- large language model +inference: false +thumbnail: >- + https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico +license: apache-2.0 +--- +# Model Card +## Github +https://github.com/chi2liu/mamba-gpt-3b + +| Metric | Value | +|-----------------------|-------| +| MMLU (5-shot) | 25.3 | +| ARC (25-shot) | 40.5 | +| HellaSwag (10-shot) | 64.9 | +| TruthfulQA (0-shot) | 37.1 | +| Avg. | 42.0 | + +We use state-of-the-art [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above. + +## Summary + +We have fine-tuned the open-lama model and surpassed the original model in multiple evaluation subtasks, making it currently the best performing 3B model with comparable performance to llama-7b +- Base model: [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b) + +## Usage + +To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed. + +```bash +pip install transformers==4.29.2 +pip install accelerate==0.19.0 +pip install torch==2.0.0 +``` + +```python +import torch +from transformers import pipeline + +generate_text = pipeline( + model="CobraMamba/mamba-gpt-3b", + torch_dtype="auto", + trust_remote_code=True, + use_fast=False, + device_map={"": "cuda:0"}, +) + +res = generate_text( + "Why is drinking water so healthy?", + min_new_tokens=2, + max_new_tokens=1024, + do_sample=False, + num_beams=1, + temperature=float(0.3), + repetition_penalty=float(1.2), + renormalize_logits=True +) +print(res[0]["generated_text"]) +``` + +You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: + +```python +print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) +``` + +```bash +<|prompt|>Why is drinking water so healthy?<|answer|> +``` + +Alternatively, you can download the mamba_gpt_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`. + +```python +import torch +from mamba_gpt_pipeline import MambaGPTTextGenerationPipeline +from transformers import AutoModelForCausalLM, AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained( + "CobraMamba/mamba-gpt-3b", + use_fast=False, + padding_side="left", + trust_remote_code=False, +) +model = AutoModelForCausalLM.from_pretrained( + "CobraMamba/mamba-gpt-3b", + torch_dtype="auto", + device_map={"": "cuda:0"}, + trust_remote_code=False, +) +generate_text = MambaGPTTextGenerationPipeline(model=model, tokenizer=tokenizer) + +res = generate_text( + "Why is drinking water so healthy?", + min_new_tokens=2, + max_new_tokens=1024, + do_sample=False, + num_beams=1, + temperature=float(0.3), + repetition_penalty=float(1.2), + renormalize_logits=True +) +print(res[0]["generated_text"]) +``` + + +You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer + +model_name = "CobraMamba/mamba-gpt-3b" # either local folder or huggingface model name +# Important: The prompt needs to be in the same format the model was trained with. +# You can find an example prompt in the experiment logs. +prompt = "<|prompt|>How are you?<|answer|>" + +tokenizer = AutoTokenizer.from_pretrained( + model_name, + use_fast=False, + trust_remote_code=False, +) +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="auto", + device_map={"": "cuda:0"}, + trust_remote_code=False, +) +model.cuda().eval() +inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") + +# generate configuration can be modified to your needs +tokens = model.generate( + **inputs, + min_new_tokens=2, + max_new_tokens=1024, + do_sample=False, + num_beams=1, + temperature=float(0.3), + repetition_penalty=float(1.2), + renormalize_logits=True +)[0] + +tokens = tokens[inputs["input_ids"].shape[1]:] +answer = tokenizer.decode(tokens, skip_special_tokens=True) +print(answer) +``` + +## Model Architecture + +``` +LlamaForCausalLM( + (model): LlamaModel( + (embed_tokens): Embedding(32000, 4096, padding_idx=0) + (layers): ModuleList( + (0-31): 32 x LlamaDecoderLayer( + (self_attn): LlamaAttention( + (q_proj): Linear(in_features=4096, out_features=4096, bias=False) + (k_proj): Linear(in_features=4096, out_features=4096, bias=False) + (v_proj): Linear(in_features=4096, out_features=4096, bias=False) + (o_proj): Linear(in_features=4096, out_features=4096, bias=False) + (rotary_emb): LlamaRotaryEmbedding() + ) + (mlp): LlamaMLP( + (gate_proj): Linear(in_features=4096, out_features=11008, bias=False) + (down_proj): Linear(in_features=11008, out_features=4096, bias=False) + (up_proj): Linear(in_features=4096, out_features=11008, bias=False) + (act_fn): SiLUActivation() + ) + (input_layernorm): LlamaRMSNorm() + (post_attention_layernorm): LlamaRMSNorm() + ) + ) + (norm): LlamaRMSNorm() + ) + (lm_head): Linear(in_features=4096, out_features=32000, bias=False) +) +``` + +## Evaluation +We evaluated OpenLLaMA on a wide range of tasks using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). The LLaMA results are generated by running the original LLaMA model on the same evaluation metrics. We note that our results for the LLaMA model differ slightly from the original LLaMA paper, which we believe is a result of different evaluation protocols. Similar differences have been reported in [this issue of lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/issues/443). Additionally, we present the results of GPT-J, a 6B parameter model trained on the [Pile](https://pile.eleuther.ai/) dataset by [EleutherAI](https://www.eleuther.ai/). + +The original LLaMA model was trained for 1 trillion tokens and GPT-J was trained for 500 billion tokens. We present the results in the table below. OpenLLaMA exhibits comparable performance to the original LLaMA and GPT-J across a majority of tasks, and outperforms them in some tasks. + + +| **Task/Metric** | finetuned-GPT 3B | OpenLLaMA 3B | +| ---------------------- | -------- | ------------ | +| anli_r1/acc | **0.35** | 0.33 | +| anli_r2/acc | **0.33** | 0.32 | +| anli_r3/acc | 0.35 | 0.35 | +| arc_challenge/acc | **0.35** | 0.34 | +| arc_challenge/acc_norm | 0.37 | 0.37 | +| arc_easy/acc | **0.71** | 0.69 | +| arc_easy/acc_norm | 0.65 | 0.65 | +| boolq/acc | **0.72** | 0.66 | +| hellaswag/acc | **0.49** | 0.43 | +| hellaswag/acc_norm | 0.66 | **0.67** | +| openbookqa/acc | 0.26 | **0.27** | +| openbookqa/acc_norm | 0.40 | 0.40 | +| piqa/acc | **0.76** | 0.75 | +| piqa/acc_norm | 0.76 | 0.76 | +| record/em | 0.88 | 0.88 | +| record/f1 | 0.88 | **0.89** | +| rte/acc | 0.55 | **0.58** | +| truthfulqa_mc/mc1 | **0.27** | 0.22 | +| truthfulqa_mc/mc2 | **0.37** | 0.35 | +| wic/acc | **0.49** | 0.48 | +| winogrande/acc | **0.63** | 0.62 | +| Average | **0.53** | 0.52 | + + +We removed the task CB and WSC from our benchmark, as our model performs suspiciously well on these two tasks. We hypothesize that there could be a benchmark data contamination in the training set. + +## Disclaimer + +Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. + +- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. +- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. +- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. +- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. +- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. +- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. + +By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. diff --git a/config.json b/config.json new file mode 100644 index 0000000..2dc1255 --- /dev/null +++ b/config.json @@ -0,0 +1,25 @@ +{ + "_name_or_path": "openlm-research/open_llama_3b", + "architectures": [ + "LlamaForCausalLM" + ], + "attention_probs_dropout_prob": 0.0, + "bos_token_id": 1, + "eos_token_id": 2, + "hidden_act": "silu", + "hidden_dropout_prob": 0.0, + "hidden_size": 3200, + "initializer_range": 0.02, + "intermediate_size": 8640, + "max_position_embeddings": 2048, + "model_type": "llama", + "num_attention_heads": 32, + "num_hidden_layers": 26, + "pad_token_id": 0, + "rms_norm_eps": 1e-06, + "tie_word_embeddings": false, + "torch_dtype": "float16", + "transformers_version": "4.29.2", + "use_cache": true, + "vocab_size": 32000 +} diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..9eadab4 --- /dev/null +++ b/generation_config.json @@ -0,0 +1,7 @@ +{ + "_from_model_config": true, + "bos_token_id": 1, + "eos_token_id": 2, + "pad_token_id": 0, + "transformers_version": "4.29.2" +} diff --git a/mamba_gpt_pipeline.py b/mamba_gpt_pipeline.py new file mode 100644 index 0000000..8d7ae7d --- /dev/null +++ b/mamba_gpt_pipeline.py @@ -0,0 +1,42 @@ +from transformers import TextGenerationPipeline +from transformers.pipelines.text_generation import ReturnType + +STYLE = "<|prompt|>{instruction}<|answer|>" + + +class MambaGPTTextGenerationPipeline(TextGenerationPipeline): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.prompt = STYLE + + def preprocess( + self, prompt_text, prefix="", handle_long_generation=None, **generate_kwargs + ): + prompt_text = self.prompt.format(instruction=prompt_text) + return super().preprocess( + prompt_text, + prefix=prefix, + handle_long_generation=handle_long_generation, + **generate_kwargs, + ) + + def postprocess( + self, + model_outputs, + return_type=ReturnType.FULL_TEXT, + clean_up_tokenization_spaces=True, + ): + records = super().postprocess( + model_outputs, + return_type=return_type, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + ) + for rec in records: + rec["generated_text"] = ( + rec["generated_text"] + .split("<|answer|>")[1] + .strip() + .split("<|prompt|>")[0] + .strip() + ) + return records diff --git a/model.safetensors b/model.safetensors new file mode 100644 index 0000000..63f1065 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b4fe205eea08add7a00632634f3501814eea3c118ef400feaba0eb50e4f4ecb9 +size 6852982288 diff --git a/pytorch_model.bin b/pytorch_model.bin new file mode 100644 index 0000000..896c7c4 --- /dev/null +++ b/pytorch_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:61bfe8e444206ea7341123d9b1e125955b94efd240016e75c5664271bdde92c0 +size 6853041741 diff --git a/special_tokens_map.json b/special_tokens_map.json new file mode 100644 index 0000000..82ad13f --- /dev/null +++ b/special_tokens_map.json @@ -0,0 +1,26 @@ +{ + "bos_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": "", + "eos_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/tokenizer.model b/tokenizer.model new file mode 100644 index 0000000..6236f6a --- /dev/null +++ b/tokenizer.model @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:81c4a3c9a9bbad64636d93660b6982940cec979a398f42684ba7194d118a3f21 +size 534194 diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 0000000..f051c6a --- /dev/null +++ b/tokenizer_config.json @@ -0,0 +1,34 @@ +{ + "add_bos_token": true, + "add_eos_token": false, + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "clean_up_tokenization_spaces": false, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "model_max_length": 2048, + "pad_token": null, + "sp_model_kwargs": {}, + "tokenizer_class": "LlamaTokenizer", + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +}