danielpark
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Commit
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Parent(s):
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init: weights
Browse files- README.md +168 -0
- config.json +48 -0
- configuration_jamba.py +0 -0
- generation_config.json +7 -0
- model-00001-of-00004.safetensors +3 -0
- model-00002-of-00004.safetensors +3 -0
- model-00003-of-00004.safetensors +3 -0
- model-00004-of-00004.safetensors +3 -0
- model.safetensors.index.json +522 -0
- modeling_jamba.py +0 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +47 -0
README.md
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---
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license: apache-2.0
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---
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---
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library_name: transformers
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license: apache-2.0
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tags:
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- jamba
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- mamba
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- moe
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---
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# Jamba-v0.1-9B
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A dense version of [Jamba-v0.1](https://huggingface.co/ai21labs/Jamba-v0.1), which extracts the weights of the first expert.
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It no longer uses MoE. Please refer to [this script](https://github.com/TechxGenus/Jamba-utils/blob/main/dense_downcycling.py) for details.
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It can use single 3090/4090 for inference, and the usage method is exactly the same as Jamba-v0.1.
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---
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# Original Model Card for Jamba
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Jamba is a state-of-the-art, hybrid SSM-Transformer LLM. It delivers throughput gains over traditional Transformer-based models, while outperforming or matching the leading models of its size class on most common benchmarks.
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Jamba is the first production-scale Mamba implementation, which opens up interesting research and application opportunities. While this initial experimentation shows encouraging gains, we expect these to be further enhanced with future optimizations and explorations.
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This model card is for the base version of Jamba. It’s a pretrained, mixture-of-experts (MoE) generative text model, with 12B active parameters and a total of 52B parameters across all experts. It supports a 256K context length, and can fit up to 140K tokens on a single 80GB GPU.
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For full details of this model please read the [release blog post](https://www.ai21.com/blog/announcing-jamba).
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## Model Details
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- **Developed by:** [AI21](https://www.ai21.com)
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- **Model type:** Joint Attention and Mamba (Jamba)
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- **License:** Apache 2.0
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- **Context length:** 256K
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- **Knowledge cutoff date:** March 5, 2024
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## Usage
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### Presequities
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Jamba requires you use `transformers` version 4.39.0 or higher:
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```bash
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pip install transformers>=4.39.0
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```
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In order to run optimized Mamba implementations, you first need to install `mamba-ssm` and `causal-conv1d`:
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```bash
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pip install mamba-ssm causal-conv1d>=1.2.0
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```
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You also have to have the model on a CUDA device.
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You can run the model not using the optimized Mamba kernels, but it is **not** recommended as it will result in significantly lower latencies. In order to do that, you'll need to specify `use_mamba_kernels=False` when loading the model.
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### Run the model
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Please note that, at the moment, `trust_remote_code=True` is required for running the new Jamba architecture.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("ai21labs/Jamba-v0.1",
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trust_remote_code=True)
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tokenizer = AutoTokenizer.from_pretrained("ai21labs/Jamba-v0.1")
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input_ids = tokenizer("In the recent Super Bowl LVIII,", return_tensors='pt').to(model.device)["input_ids"]
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outputs = model.generate(input_ids, max_new_tokens=216)
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print(tokenizer.batch_decode(outputs))
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# ["<|startoftext|>In the recent Super Bowl LVIII, the Kansas City Chiefs emerged victorious, defeating the San Francisco 49ers in a thrilling overtime showdown. The game was a nail-biter, with both teams showcasing their skills and determination.\n\nThe Chiefs, led by their star quarterback Patrick Mahomes, displayed their offensive prowess, while the 49ers, led by their strong defense, put up a tough fight. The game went into overtime, with the Chiefs ultimately securing the win with a touchdown.\n\nThe victory marked the Chiefs' second Super Bowl win in four years, solidifying their status as one of the top teams in the NFL. The game was a testament to the skill and talent of both teams, and a thrilling end to the NFL season.\n\nThe Super Bowl is not just about the game itself, but also about the halftime show and the commercials. This year's halftime show featured a star-studded lineup, including Usher, Alicia Keys, and Lil Jon. The show was a spectacle of music and dance, with the performers delivering an energetic and entertaining performance.\n"]
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```
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<details>
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<summary><strong>Loading the model in half precision</strong></summary>
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The published checkpoint is saved in BF16. In order to load it into RAM in BF16/FP16, you need to specify `torch_dtype`:
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```python
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from transformers import AutoModelForCausalLM
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import torch
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model = AutoModelForCausalLM.from_pretrained("ai21labs/Jamba-v0.1",
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trust_remote_code=True,
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torch_dtype=torch.bfloat16) # you can also use torch_dtype=torch.float16
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```
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When using half precision, you can enable the [FlashAttention2](https://github.com/Dao-AILab/flash-attention) implementation of the Attention blocks. In order to use it, you also need the model on a CUDA device. Since in this precision the model is to big to fit on a single 80GB GPU, you'll also need to parallelize it using [accelerate](https://huggingface.co/docs/accelerate/index):
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```python
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from transformers import AutoModelForCausalLM
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import torch
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model = AutoModelForCausalLM.from_pretrained("ai21labs/Jamba-v0.1",
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trust_remote_code=True,
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torch_dtype=torch.bfloat16,
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attn_implementation="flash_attention_2",
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device_map="auto")
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```
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</details>
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<details><summary><strong>Load the model in 8-bit</strong></summary>
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**Using 8-bit precision, it is possible to fit up to 140K sequence lengths on a single 80GB GPU.** You can easily quantize the model to 8-bit using [bitsandbytes](https://huggingface.co/docs/bitsandbytes/index). In order to not degrade model quality, we recommend to exclude the Mamba blocks from the quantization:
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```python
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from transformers import AutoModelForCausalLM, BitsAndBytesConfig
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quantization_config = BitsAndBytesConfig(load_in_8bit=True,
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llm_int8_skip_modules=["mamba"])
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model = AutoModelForCausalLM.from_pretrained("ai21labs/Jamba-v0.1",
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trust_remote_code=True,
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torch_dtype=torch.bfloat16,
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attn_implementation="flash_attention_2",
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quantization_config=quantization_config)
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```
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</details>
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### Fine-tuning example
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Jamba is a base model that can be fine-tuned for custom solutions (including for chat/instruct versions). You can fine-tune it using any technique of your choice. Here is an example of fine-tuning with the [PEFT](https://huggingface.co/docs/peft/index) library:
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```python
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from datasets import load_dataset
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from trl import SFTTrainer
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from peft import LoraConfig
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from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
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tokenizer = AutoTokenizer.from_pretrained("ai21labs/Jamba-v0.1")
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model = AutoModelForCausalLM.from_pretrained("ai21labs/Jamba-v0.1", trust_remote_code=True, device_map='auto')
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dataset = load_dataset("Abirate/english_quotes", split="train")
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training_args = TrainingArguments(
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output_dir="./results",
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num_train_epochs=3,
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per_device_train_batch_size=4,
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logging_dir='./logs',
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logging_steps=10,
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learning_rate=2e-3
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)
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lora_config = LoraConfig(
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r=8,
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target_modules=["embed_tokens", "x_proj", "in_proj", "out_proj"],
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task_type="CAUSAL_LM",
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bias="none"
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)
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trainer = SFTTrainer(
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model=model,
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tokenizer=tokenizer,
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args=training_args,
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peft_config=lora_config,
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train_dataset=dataset,
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dataset_text_field="quote",
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)
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trainer.train()
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```
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## Results on common benchmarks
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| Benchmark | Score |
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|--------------|:-----:|
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| HellaSwag | 87.1% |
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| Arc Challenge | 64.4% |
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| WinoGrande | 82.5% |
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| PIQA | 83.2% |
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| MMLU | 67.4% |
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| BBH | 45.4% |
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| TruthfulQA | 46.4% |
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| GSM8K (CoT) | 59.9% |
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It's crucial that the 'BOS' token is added to all prompts, which might not be enabled by default in all eval frameworks.
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## Notice
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Jamba is a pretrained base model and did not undergo any alignment for instruct/chat interactions.
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As a base model, Jamba is intended for use as a foundation layer for fine tuning, training, and developing custom solutions. Jamba does not have safety moderation mechanisms and guardrails should be added for responsible and safe use.
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## About AI21
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AI21 builds reliable, practical, and scalable AI solutions for the enterprise.
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Jamba is the first in AI21’s new family of models, and the Instruct version of Jamba is available in beta via the [AI21 platform](https://www.ai21.com/studio).
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config.json
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{
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"architectures": [
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"JambaForCausalLM"
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],
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"attention_dropout": 0.0,
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"attn_layer_offset": 4,
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"attn_layer_period": 8,
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"auto_map": {
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"AutoConfig": "configuration_jamba.JambaConfig",
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"AutoModel": "modeling_jamba.JambaModel",
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"AutoModelForCausalLM": "modeling_jamba.JambaForCausalLM",
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"AutoModelForSequenceClassification": "model.JambaForSequenceClassification"
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},
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"bos_token_id": 1,
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"calc_logits_for_entire_prompt": false,
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"eos_token_id": 2,
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"expert_layer_offset": 1,
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"expert_layer_period": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"mamba_conv_bias": true,
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"mamba_d_conv": 4,
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"mamba_d_state": 16,
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"mamba_dt_rank": 256,
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"mamba_expand": 2,
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"mamba_inner_layernorms": true,
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"mamba_proj_bias": false,
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"model_type": "jamba",
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"n_ctx": 262144,
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"num_attention_heads": 32,
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"num_experts": 1,
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"num_experts_per_tok": 1,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"output_router_logits": false,
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"pad_token_id": 0,
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"rms_norm_eps": 1e-06,
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"router_aux_loss_coef": 0.001,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.39.3",
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"use_cache": true,
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"use_mamba_kernels": true,
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"vocab_size": 65536
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}
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configuration_jamba.py
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Binary file (11.2 kB). View file
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"pad_token_id": 0,
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"transformers_version": "4.39.3"
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}
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model.safetensors.index.json
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modeling_jamba.py
ADDED
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special_tokens_map.json
ADDED
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tokenizer.json
ADDED
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tokenizer.model
ADDED
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version https://git-lfs.github.com/spec/v1
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size 1124742
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tokenizer_config.json
ADDED
@@ -0,0 +1,47 @@
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