Update v3.0

README.md

@@ -5,21 +5,18 @@ language:
 pipeline_tag: text-generation
 library_name: transformers
 tags:
-- alpaca
 - llama
 - LLM
-datasets:
-- tatsu-lab/alpaca
 inference: false
 ---
 
 <p align="center" width="100%">
-<img src="https://huggingface.co/bofenghuang/vigogne-instruct-7b/resolve/main/vigogne_logo.png" alt="Vigogne" style="width: 40%; min-width: 300px; display: block; margin: auto;">
+<img src="https://huggingface.co/bofenghuang/vigogne-7b-instruct/resolve/main/vigogne_logo.png" alt="Vigogne" style="width: 40%; min-width: 300px; display: block; margin: auto;">
 </p>
 
-# Vigogne-instruct-7b: A French Instruction-following LLaMA Model
+# Vigogne-7B-Instruct: A French Instruction-following LLaMA Model
 
-Vigogne-instruct-7b is a LLaMA-7B model fine-tuned to follow the 🇫🇷 French instructions.
+Vigogne-7B-Instruct is a LLaMA-7B model fine-tuned to follow the French instructions.
 
 For more information, please visit the Github repo: https://github.com/bofenghuang/vigogne
 
@@ -30,20 +27,7 @@ For more information, please visit the Github repo: https://github.com/bofenghua
 This repo only contains the low-rank adapter. In order to access the complete model, you also need to load the base LLM model and tokenizer.
 
 ```python
-from peft import PeftModel
-from transformers import LlamaForCausalLM, LlamaTokenizer
-
-base_model_name_or_path = "name/or/path/to/hf/llama/7b/model"
-lora_model_name_or_path = "bofenghuang/vigogne-instruct-7b"
-
-tokenizer = LlamaTokenizer.from_pretrained(base_model_name_or_path, padding_side="right", use_fast=False)
-model = LlamaForCausalLM.from_pretrained(
-    base_model_name_or_path,
-    load_in_8bit=True,
-    torch_dtype=torch.float16,
-    device_map="auto",
-)
-model = PeftModel.from_pretrained(model, lora_model_name_or_path)
+
 ```
 
 You can infer this model by using the following Google Colab Notebook.

adapter_config.json

@@ -1,22 +0,0 @@
-{
-  "base_model_name_or_path": "huggyllama/llama-7b",
-  "bias": "none",
-  "fan_in_fan_out": false,
-  "inference_mode": true,
-  "init_lora_weights": true,
-  "lora_alpha": 16,
-  "lora_dropout": 0.05,
-  "modules_to_save": null,
-  "peft_type": "LORA",
-  "r": 8,
-  "target_modules": [
-    "q_proj",
-    "v_proj",
-    "k_proj",
-    "o_proj",
-    "gate_proj",
-    "down_proj",
-    "up_proj"
-  ],
-  "task_type": "CAUSAL_LM"
-}

config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "huggyllama/llama-7b",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 2048,
+  "max_sequence_length": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.28.1",
+  "use_cache": true,
+  "vocab_size": 32000
+}

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": "4.28.1"
+}

runs/1/events.out.tfevents.1683597421.koios.zaion.ai.956903.2 → pytorch_model-00001-of-00002.bin

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