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bobtk
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- .DS_Store +0 -0
- README.md +25 -0
- config.json +32 -0
- model-00001-of-00011.safetensors +3 -0
- model-00002-of-00011.safetensors +3 -0
- model-00003-of-00011.safetensors +3 -0
- model-00004-of-00011.safetensors +3 -0
- model-00005-of-00011.safetensors +3 -0
- model-00006-of-00011.safetensors +3 -0
- model-00007-of-00011.safetensors +3 -0
- model-00008-of-00011.safetensors +3 -0
- model-00009-of-00011.safetensors +3 -0
- model-00010-of-00011.safetensors +3 -0
- model-00011-of-00011.safetensors +3 -0
- model.safetensors.index.json +0 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer_config.json +46 -0
.DS_Store
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Binary file (6.15 kB). View file
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README.md
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---
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language:
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- ja
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- en
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license: mit
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library_name: transformers
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tags:
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- mlx
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---
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# mlx-community/stockmark-100b-4bit
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This model was converted to MLX format from [`stockmark/stockmark-100b`]() using mlx-lm version **0.12.1**.
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Refer to the [original model card](https://huggingface.co/stockmark/stockmark-100b) for more details on the model.
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## Use with mlx
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```bash
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pip install mlx-lm
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```
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("mlx-community/stockmark-100b-4bit")
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response = generate(model, tokenizer, prompt="hello", verbose=True)
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```
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"eos_token_id": 1,
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"hidden_act": "silu",
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"hidden_size": 9216,
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"initializer_range": 0.02,
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"intermediate_size": 32256,
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"max_position_embeddings": 4096,
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"model_type": "llama",
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"num_attention_heads": 72,
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"num_hidden_layers": 88,
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"num_key_value_heads": 8,
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"pad_token_id": 2,
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"pretraining_tp": 1,
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"quantization": {
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"group_size": 64,
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"bits": 4
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},
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.40.1",
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"use_cache": true,
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"vocab_size": 60416
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}
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model-00001-of-00011.safetensors
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model.safetensors.index.json
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special_tokens_map.json
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tokenizer.json
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tokenizer_config.json
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