rename

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "./tiny-mistral",
+  "_name_or_path": "./micro_mistral",
   "architectures": [
     "MistralForCausalLM"
   ],
@@ -26,4 +26,4 @@
   "transformers_version": "4.34.1",
   "use_cache": true,
   "vocab_size": 50304
-}
+}

init_model.py

@@ -23,4 +23,4 @@ model = model.to(
 )  # Converts all floating point parameters to bfloat16
 
 # Save the model with SafeTensors
-model.save_pretrained("./model_bf16.safetensors", save_in_safe_tensors_format=True)
+model.save_pretrained("./micro_mistral", save_in_safe_tensors_format=True)

micro_mistral/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb30b5b14f1785c3e9fc4fb7e26e33d8bffe091da6f9203cf637b27515b5bc4f
+size 244613320

test.py

@@ -0,0 +1,16 @@
+# Load model directly and generate text from a test string
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("Nbardy/mini-mistral")
+model = AutoModelForCausalLM.from_pretrained("Nbardy/mini-mistral")
+
+# Prepare the test string for input
+input_text = "This is a test string"
+input_ids = tokenizer.encode(input_text, return_tensors="pt")
+
+# Generate text using the model
+output = model.generate(input_ids)
+
+# Decode and print the generated text
+generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+print(generated_text)