first commit

actor/README.md

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+---
+license: apache-2.0
+pipeline_tag: text-generation
+language:
+- en
+library_name: transformers
+---
+## Example Usage
+
+### Prompt format:
+
+```
+SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
+USER: How is insulin synthesized?
+ASSISTANT:
+```
+
+### Code example:
+
+```python
+import torch, json
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_path = "migtissera/SynthIA-7B-v2.0"
+output_file_path = "./SynthIA-7B-v2.0-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": 0.75,
+        "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,
+        )
+    output = rest[0][length:]
+    string = tokenizer.decode(output, skip_special_tokens=True)
+    answer = string.split("USER:")[0].strip()
+    return f"{answer}"
+
+
+conversation = f"SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. 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")
+
+```

actor/config.json

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+{
+  "_name_or_path": "/home/ubuntu/llm/Mistral-7B-v0.1",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.34.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

actor/generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.34.0.dev0"
+}

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