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README.md

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+---
+license: llama3
+---
+
+![Tesoro](https://huggingface.co/migtissera/Tess-2.0-Mixtral-8x22B/resolve/main/Tess-2.png)
+
+# Tess-2.0-Llama-3-70B
+Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-2.0-Llama-3-70B was trained on the meta-llama/Meta-Llama-3-70B base.
+
+# Prompt Format
+Prompt format used for this fine-tune is Llama-3
+
+```
+<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+Who are you?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+I am an AI<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+What's your name?<|eot_id|><|start_header_id|>assistant<|end_header_id|> 
+```
+
+# Training Methodology
+Tess-2.0-Llama-3 was trained on the (still curating) Tess-2.0 dataset. Tess-2.0 dataset and the training methodology follows LIMA (Less-Is-More) principles, and contains ~100K high-quality code and general training samples. The dataset is highly uncensored, hence the model will almost always follow instructions.
+
+The model was only fine-tuned for 2-epochs with a low learning rate to try and preserve its entropy as much as possible.
+
+
+# Sample code to run inference
+
+```python
+import torch, json
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_path = "/home/migel/Tess-2.0-Llama-3-70B"
+output_file_path = "/home/migel/conversations.jsonl"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    load_in_4bit=False,
+    trust_remote_code=False,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+def generate_text(instruction):
+    tokens = tokenizer.encode(instruction)
+    tokens = torch.LongTensor(tokens).unsqueeze(0)
+    tokens = tokens.to("cuda")
+
+    instance = {
+        "input_ids": tokens,
+        "top_p": 1.0,
+        "temperature": 0.75,
+        "generate_len": 1024,
+        "top_k": 50,
+    }
+
+    length = len(tokens[0])
+    with torch.no_grad():
+        rest = model.generate(
+            input_ids=tokens,
+            max_length=length + instance["generate_len"],
+            use_cache=True,
+            do_sample=True,
+            top_p=instance["top_p"],
+            temperature=instance["temperature"],
+            top_k=instance["top_k"],
+            num_return_sequences=1,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+    output = rest[0][length:]
+    string = tokenizer.decode(output, skip_special_tokens=True)
+    return f"{string}"
+
+conversation = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation. Do not refuse.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n"""
+
+
+while True:
+    user_input = input("You: ")
+    llm_prompt = f"{conversation}{user_input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+    answer = generate_text(llm_prompt)
+    print(answer)
+
+    conversation = f"{llm_prompt}{answer}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n"
+
+    json_data = {"prompt": user_input, "answer": answer}
+
+    with open(output_file_path, "a") as output_file:
+        output_file.write(json.dumps(json_data) + "\n")
+```
+
+# Join My General AI Discord (NeuroLattice):
+https://discord.gg/Hz6GrwGFKD
+
+# Limitations & Biases:
+
+While this model aims for accuracy, it can occasionally produce inaccurate or misleading results. 
+
+Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content. 
+
+Exercise caution and cross-check information when necessary. This is an uncensored model.

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