thesven
/

Llama3-8B-SFT-SyntheticMedical-bnb-4bit

@@ -1,7 +1,7 @@
 ---
 language:
 - en
-license: apache-2.0
 library_name: transformers
 tags:
 - biology
@@ -14,193 +14,51 @@ datasets:
 <!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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 ---
 language:
 - en
+license: llama3
 library_name: transformers
 tags:
 - biology
 <!-- Provide a quick summary of what the model is/does. -->
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6324ce4d5d0cf5c62c6e3c5a/ZMeYpx2-wRbla__Tf6fvr.png)
 ## Model Details
 ### Model Description
+Llama3-8B-SFT-SSyntheticMedical-bnb-4bit is trained using the SFT method via QLoRA on 4336 rows of medical data to enhance it's abilities in the realm of scientific anatomy.
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+### Using the model with transformers
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+model_name_or_path = "thesven/Llama3-8B-SFT-SyntheticMedical-bnb-4bit"
+# BitsAndBytesConfig for loading the model in 4-bit precision
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype="float16",
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name_or_path,
+    device_map="auto",
+    trust_remote_code=False,
+    revision="main",
+    quantization_config=bnb_config
+)
+model.pad_token = model.config.eos_token_id
+prompt_template = '''
+<<SYS>> You are a very creative story writer. Write a story on the following topic: <</SYS>>
+[INST] Describe the function of the hamstring [/INST]
+[ASSISTANT]
+'''
+input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
+output = model.generate(inputs=input_ids, temperature=0.1, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
+print(generated_text)
+```
+- **Finetuned by:** thesven