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Model: Joseph717171/Tess-10.7B-v2.0
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: []
+library_name: transformers
+tags:
+- mergekit
+- merge
+pipeline_tag: text-generation
+---
+# Credit for the model card's description goes to ddh0, mergekit, and, migtissera
+# Inspired by ddh0/Starling-LM-10.7B-beta and ddh0/Mistral-10.7B-Instruct-v0.2
+# Tess-10.7B-v0.2
+
+# Deprecated
+"This model is deprecated due to the use of wrong sliding window parameter while training. Will update with the new model link in a couple of days." - migtissera
+
+This is Tess-10.7B-v0.2, a depth-upscaled version of [migtissera/Tess-7B-v2.0](https://huggingface.co/migtissera/Tess-7B-v2.0).
+
+This model is intended to be used as a basis for further fine-tuning, or as a drop-in upgrade from the original 7 billion parameter model.
+
+Paper detailing how Depth-Up Scaling works:  [SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling](https://arxiv.org/abs/2312.15166)
+
+This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+
+
+# Prompt format same as [migtissera/Tess-7B-v2.0](https://huggingface.co/migtissera/Tess-7B-v2.0)
+
+# Prompt Format:
+
+```
+SYSTEM: <ANY SYSTEM CONTEXT>
+USER: 
+ASSISTANT:
+```
+
+
+## Merge Details
+### Merge Method
+
+This model was merged using the passthrough merge method.
+
+### Models Merged
+
+The following models were included in the merge:
+* /Users/jsarnecki/opt/migtissera/Tess-7B-v2.0
+
+### Configuration
+
+The following YAML configuration was used to produce this model:
+
+```yaml
+dtype: bfloat16
+merge_method: passthrough
+slices:
+- sources:
+  - layer_range: [0, 24]
+    model: /Users/jsarnecki/opt/migtissera/Tess-7B-v2.0
+- sources:
+  - layer_range: [8, 32]
+    model: /Users/jsarnecki/opt/migtissera/Tess-7B-v2.0 
+
+```
+# GGUFs (Thanks to [bartowski](https://huggingface.co/bartowski))
+
+https://huggingface.co/bartowski/Tess-10.7B-v2.0-GGUF
+
+# exl2s (Thanks to [bartowski](https://huggingface.co/bartowski))
+
+https://huggingface.co/bartowski/Tess-10.7B-v2.0-exl2
+
+![Tesoro](https://huggingface.co/migtissera/Tess-7B-v2.0/resolve/main/Tesoro.png)
+
+---
+license: apache-2.0
+---
+
+# Tess-7B-v2.0
+Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-7B-v2.0 was trained on the Mistral-7B-v0.2 base.
+
+# Prompt Format:
+
+```
+SYSTEM: <ANY SYSTEM CONTEXT>
+USER: 
+ASSISTANT:
+```
+
+### Below shows a code example on how to use this model:
+
+```python
+import torch, json
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_path = "migtissera/Tess-7B-v2.0"
+output_file_path = "./conversations.jsonl"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    load_in_8bit=False,
+    trust_remote_code=True,
+)
+
+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": 0.5,
+        "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,
+        )
+    output = rest[0][length:]
+    string = tokenizer.decode(output, skip_special_tokens=True)
+    answer = string.split("USER:")[0].strip()
+    return f"{answer}"
+
+
+conversation = f"SYSTEM: Answer the question thoughtfully and intelligently. Always answer without hesitation."
+
+
+while True:
+    user_input = input("You: ")
+    llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
+    answer = generate_text(llm_prompt)
+    print(answer)
+    conversation = f"{llm_prompt}{answer}"
+    json_data = {"prompt": user_input, "answer": answer}
+
+    ## Save your conversation
+    with open(output_file_path, "a") as output_file:
+        output_file.write(json.dumps(json_data) + "\n")
+
+```
+
+<br>
+
+#### 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.
+
+
+<br>