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config.json

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+{
+  "_name_or_path": "/Users/migueldeguzman/Desktop/papercliptodd/falcon-1b/v2/",
+  "alibi": true,
+  "apply_residual_connection_post_layernorm": false,
+  "architectures": [
+    "FalconForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_falcon.FalconConfig",
+    "AutoModel": "modeling_falcon.FalconModel",
+    "AutoModelForCausalLM": "modeling_falcon.FalconForCausalLM",
+    "AutoModelForQuestionAnswering": "modeling_falcon.FalconForQuestionAnswering",
+    "AutoModelForSequenceClassification": "modeling_falcon.FalconForSequenceClassification",
+    "AutoModelForTokenClassification": "modeling_falcon.FalconForTokenClassification"
+  },
+  "bias": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_dropout": 0.0,
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "max_position_embeddings": 2048,
+  "model_type": "falcon",
+  "multi_query": false,
+  "new_decoder_architecture": false,
+  "num_attention_heads": 32,
+  "num_hidden_layers": 24,
+  "num_kv_heads": 32,
+  "parallel_attn": false,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.3",
+  "use_cache": true,
+  "vocab_size": 50304
+}

generate.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+def main():
+    # Load the fine-tuned model and tokenizer
+    model_output_dir = "/Users/migueldeguzman/Desktop/papercliptodd/falcon-1b/v3/"  # Replace with your fine-tuned model directory
+    tokenizer = AutoTokenizer.from_pretrained(model_output_dir)
+    model = AutoModelForCausalLM.from_pretrained(model_output_dir)
+
+    while True:
+        # User input for text generation prompt
+        prompt = input("Enter a prompt for text generation (or type 'exit' to quit): ")
+        
+        if prompt.lower() == 'exit':
+            break
+
+        # Encode the prompt and generate text
+        input_ids = tokenizer.encode(prompt, return_tensors="pt")
+        output = model.generate(
+            input_ids,
+            max_length=1024,
+            num_return_sequences=1,
+            no_repeat_ngram_size=2,
+            top_k=50,
+            top_p=0.95,
+            temperature=0.001
+        )
+
+        # Decode and print the generated text
+        generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+        print("Generated Text:")
+        print(generated_text)
+
+if __name__ == "__main__":
+    main()

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.33.3"
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:da012fda21c62d8ff4f097c638723d3664d4014e998c7ff8ccce02e8dbcfd328
+size 5246593815

special_tokens_map.json

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+{
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "unk_token": "<|endoftext|>"
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "model_max_length": 1024,
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}