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README.md

@@ -1,3 +1,21 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+# TriLM 1.5B Unpacked
+
+TriLM (ternary model), unpacked to FP16 format - compatible with FP16 GEMMs. After unpacking, TriLM has the same architecture as LLaMa.
+
+```python
+import transformers as tf, torch
+model_name = "SpectraSuite/TriLM_1.5B_Unpacked"
+
+# Please adjust the temperature, repetition penalty, top_k, top_p and other sampling parameters according to your needs.
+pipeline = tf.pipeline("text-generation", model=model_id, model_kwargs={"torch_dtype": torch.float16}, device_map="auto")
+
+# These are base (pretrained) LLMs that are not instruction and chat tuned. You may need to adjust your prompt accordingly.
+pipeline("Once upon a time")
+```
+
+* License: Apache 2.0
+* 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

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dims_per_head": 64,
+  "eos_token_id": 0,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 32,
+  "pretraining_tp": 1,
+  "pretraining_model_parallel": 2,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "rotary_emb_base": 10000,
+  "rotary_pct": 1.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.40.1",
+  "use_cache": true,
+  "use_parallel_residual": false,
+  "vocab_size": 50432
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "transformers_version": "4.40.1"
+}

model.safetensors

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+size 3030610416

special_tokens_map.json

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+}

tokenizer_config.json

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+}