初始化项目，由ModelHub XC社区提供模型
Model: SpectraSuite/TriLM_1.1B_Unpacked Source: Original Platform
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README.md
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---
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license: apache-2.0
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---
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# TriLM 1.1B Unpacked
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TriLM (ternary model), unpacked to FP16 format - compatible with FP16 GEMMs. After unpacking, TriLM has the same architecture as LLaMa.
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```python
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import transformers as tf, torch
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model_name = "SpectraSuite/TriLM_1.1B_Unpacked"
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# Please adjust the temperature, repetition penalty, top_k, top_p and other sampling parameters according to your needs.
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pipeline = tf.pipeline("text-generation", model=model_id, model_kwargs={"torch_dtype": torch.float16}, device_map="auto")
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# These are base (pretrained) LLMs that are not instruction and chat tuned. You may need to adjust your prompt accordingly.
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pipeline("Once upon a time")
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```
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* License: Apache 2.0
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* We will use our GitHub repo for communication (including HF repo related queries). Feel free to open an issue here https://github.com/NolanoOrg/SpectraSuite
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config.json
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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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"dims_per_head": 64,
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"hidden_act": "silu",
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"hidden_size": 1792,
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"initializer_range": 0.02,
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"intermediate_size": 5120,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 2048,
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"model_type": "llama",
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"num_attention_heads": 28,
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"num_hidden_layers": 24,
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"num_key_value_heads": 28,
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"pretraining_tp": 1,
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"pretraining_model_parallel": 2,
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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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"rotary_emb_base": 10000,
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"rotary_pct": 1.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.40.1",
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"use_cache": true,
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"use_parallel_residual": false,
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"vocab_size": 50432
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}
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generation_config.json
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{
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"_from_model_config": true,
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"transformers_version": "4.40.1"
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}
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version https://git-lfs.github.com/spec/v1
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oid sha256:ac21db61eec2eafd62279a9b494b4266cfdf1a8f0fe25d022d0810ca98757e06
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size 2299465664
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special_tokens_map.json
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{
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}
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}
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tokenizer.json
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}
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