First model version
Browse files- README.md +120 -0
- added_tokens.json +6 -0
- config.json +38 -0
- generation_config.json +7 -0
- gitattributes +35 -0
- merges.txt +0 -0
- model.safetensors.index.json +970 -0
- output-00001-of-00006.safetensors +3 -0
- output-00002-of-00006.safetensors +3 -0
- output-00003-of-00006.safetensors +3 -0
- output-00004-of-00006.safetensors +3 -0
- output-00005-of-00006.safetensors +3 -0
- output-00006-of-00006.safetensors +3 -0
- pytorch_model.bin.index.json +970 -0
- special_tokens_map.json +20 -0
- tokenizer.json +0 -0
- tokenizer_config.json +51 -0
- vocab.json +0 -0
README.md
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---
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license: other
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license_name: qwen2
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license_link: https://huggingface.co/Qwen/Qwen2-72B/blob/main/LICENSE
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---
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# Tess-v2.5.2 (Qwen2-72B)
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# Update:
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This is a fine-tune over the Tess-v2.5, with a changed learning rate and a subset of Tess-v2.5 dataset. The model is completely different to Tess-v2.5.
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I was testing a new feature with the Tess-v2.5 dataset. If you had used the model, you might have noticed that the model generations sometimes would end up with a follow-up question. This is intentional, and was created to provide more of a "natural" conversation.
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What had happened earlier was that the stop token wasn't getting properly generated, so the model would go on to answer its own question.
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This is fixed in Tess-v2.5.2. The model would still ask you follow-up questions, but the stop tokens are getting properly generated. If you'd like to not have the follow-up questions feature, just add the following to your system prompt: "No follow-up questions necessary".
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# Tess-v2.5.2 (Qwen2-72B)
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We've created Tess-v2.5.2, the latest state-of-the-art model in the Tess series of Large Language Models (LLMs). Tess, short for Tesoro (<em>Treasure</em> in Italian), is the flagship LLM series created by Migel Tissera. Tess-v2.5.2 brings significant improvements in reasoning capabilities, coding capabilities and mathematics. It is currently the #1 ranked open weight model when evaluated on MMLU (Massive Multitask Language Understanding). It scores higher than all other open weight models including Qwen2-72B-Instruct, Llama3-70B-Instruct, Mixtral-8x22B-Instruct and DBRX-Instruct. Further, when evaluated on MMLU, Tess-v2.5.2 (Qwen2-72B) model outperforms even the frontier closed models Gemini-1.0-Ultra, Gemini-1.5-Pro, Mistral-Large and Claude-3-Sonnet.
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Tess-v2.5.2 (Qwen2-72B) was fine-tuned over the newly released Qwen2-72B base, using the Tess-v2.5 dataset that contain 300K samples spanning multiple topics, including business and management, marketing, history, social sciences, arts, STEM subjects and computer programming. This dataset was synthetically generated using the [Sensei](https://github.com/migtissera/Sensei) framework, using multiple frontier models such as GPT-4-Turbo, Claude-Opus and Mistral-Large.
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The compute for this model was generously sponsored by [KindoAI](https://kindo.ai).
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When evaluated on a subset of AGIEval (Nous), this model compares very well with the godfather GPT-4-0314 model as well.
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# Training Process
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Tess-v2.5.2 model was initiated with the base weights of Qwen2-72B. It was then fine-tuned with the Tess-v2.5 dataset, using Axolotl as the training framework. Most of Tess models follow a common fine-tuning methodology: low learning rates, low number of epochs, and uses very high quality and diverse data. This model was fine-tuned on a 4xA100 VM on Microsoft Azure for 4 days. The model has not been aligned with RLHF or DPO.
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The author believes that model's capabilities seem to come primariliy from the pre-training process. This is the foundation for every fine-tune of Tess models, and preserving the entropy of the base models is of paramount to the author.
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# Sample code to run inference
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Note that this model uses ChatML prompt format.
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```python
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import torch, json
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from stop_word import StopWordCriteria
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model_path = "migtissera/Tess-v2.5.2-Qwen2-72B"
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output_file_path = "/home/migel/conversations.jsonl"
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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torch_dtype=torch.float16,
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device_map="auto",
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load_in_4bit=False,
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trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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terminators = [
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tokenizer.convert_tokens_to_ids("<|im_end|>")
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]
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def generate_text(instruction):
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tokens = tokenizer.encode(instruction)
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tokens = torch.LongTensor(tokens).unsqueeze(0)
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tokens = tokens.to("cuda")
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instance = {
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"input_ids": tokens,
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"top_p": 1.0,
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"temperature": 0.75,
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"generate_len": 1024,
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"top_k": 50,
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}
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length = len(tokens[0])
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with torch.no_grad():
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rest = model.generate(
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input_ids=tokens,
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max_length=length + instance["generate_len"],
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use_cache=True,
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do_sample=True,
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top_p=instance["top_p"],
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temperature=instance["temperature"],
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top_k=instance["top_k"],
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num_return_sequences=1,
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pad_token_id=tokenizer.eos_token_id,
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eos_token_id=terminators,
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)
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output = rest[0][length:]
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string = tokenizer.decode(output, skip_special_tokens=True)
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return f"{string}"
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conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""
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while True:
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user_input = input("You: ")
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llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
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answer = generate_text(llm_prompt)
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print(answer)
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conversation = f"{llm_prompt}{answer}\n"
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json_data = {"prompt": user_input, "answer": answer}
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with open(output_file_path, "a") as output_file:
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output_file.write(json.dumps(json_data) + "\n")
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```
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# Join My General AI Discord (NeuroLattice):
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https://discord.gg/Hz6GrwGFKD
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# Limitations & Biases:
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While this model aims for accuracy, it can occasionally produce inaccurate or misleading results.
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Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content.
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Exercise caution and cross-check information when necessary. This is an uncensored model.
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added_tokens.json
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{
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"<|end_of_text|>": 151646,
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"<|endoftext|>": 151643,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644
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}
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config.json
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{
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"_name_or_path": "/home/migel/Tess-v2.5-qwen2-72B-safetensors",
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 8192,
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"initializer_range": 0.02,
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"intermediate_size": 29568,
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"max_position_embeddings": 131072,
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"max_window_layers": 80,
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"model_type": "qwen2",
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"num_attention_heads": 64,
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"num_hidden_layers": 80,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 1000000.0,
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"sliding_window": 131072,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.41.1",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 152064,
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"quantization_config": {
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"quant_method": "exl2",
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"version": "0.1.8",
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"bits": 4.8,
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"head_bits": 6,
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"calibration": {
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"rows": 115,
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"length": 2048,
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"dataset": "(default)"
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}
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}
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}
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": 151643,
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"max_new_tokens": 2048,
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"transformers_version": "4.41.1"
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}
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gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.ckpt filter=lfs diff=lfs merge=lfs -text
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*.ftz filter=lfs diff=lfs merge=lfs -text
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*.gz filter=lfs diff=lfs merge=lfs -text
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*.h5 filter=lfs diff=lfs merge=lfs -text
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*.joblib filter=lfs diff=lfs merge=lfs -text
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
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*.mlmodel filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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*.msgpack filter=lfs diff=lfs merge=lfs -text
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*.npy filter=lfs diff=lfs merge=lfs -text
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*.npz filter=lfs diff=lfs merge=lfs -text
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*.onnx filter=lfs diff=lfs merge=lfs -text
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*.ot filter=lfs diff=lfs merge=lfs -text
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*.parquet filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pickle filter=lfs diff=lfs merge=lfs -text
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*.pkl filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.tar filter=lfs diff=lfs merge=lfs -text
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*.tflite filter=lfs diff=lfs merge=lfs -text
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*.tgz filter=lfs diff=lfs merge=lfs -text
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*.wasm filter=lfs diff=lfs merge=lfs -text
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*.xz filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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merges.txt
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The diff for this file is too large to render.
See raw diff
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model.safetensors.index.json
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| 1 |
+
{
|
| 2 |
+
"metadata": {
|
| 3 |
+
"total_size": 145412407296
|
| 4 |
+
},
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|
| 900 |
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|
| 901 |
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|
| 902 |
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|
| 903 |
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|
| 904 |
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|
| 905 |
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|
| 906 |
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|
| 907 |
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|
| 908 |
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|
| 909 |
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|
| 910 |
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"model.layers.77.mlp.gate_proj.weight": "pytorch_model-00029-of-00031.bin",
|
| 911 |
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|
| 912 |
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|
| 913 |
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|
| 914 |
+
"model.layers.77.self_attn.k_proj.weight": "pytorch_model-00029-of-00031.bin",
|
| 915 |
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"model.layers.77.self_attn.o_proj.weight": "pytorch_model-00029-of-00031.bin",
|
| 916 |
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"model.layers.77.self_attn.q_proj.bias": "pytorch_model-00029-of-00031.bin",
|
| 917 |
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"model.layers.77.self_attn.q_proj.weight": "pytorch_model-00029-of-00031.bin",
|
| 918 |
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"model.layers.77.self_attn.v_proj.bias": "pytorch_model-00029-of-00031.bin",
|
| 919 |
+
"model.layers.77.self_attn.v_proj.weight": "pytorch_model-00029-of-00031.bin",
|
| 920 |
+
"model.layers.78.input_layernorm.weight": "pytorch_model-00030-of-00031.bin",
|
| 921 |
+
"model.layers.78.mlp.down_proj.weight": "pytorch_model-00030-of-00031.bin",
|
| 922 |
+
"model.layers.78.mlp.gate_proj.weight": "pytorch_model-00030-of-00031.bin",
|
| 923 |
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"model.layers.78.mlp.up_proj.weight": "pytorch_model-00030-of-00031.bin",
|
| 924 |
+
"model.layers.78.post_attention_layernorm.weight": "pytorch_model-00030-of-00031.bin",
|
| 925 |
+
"model.layers.78.self_attn.k_proj.bias": "pytorch_model-00029-of-00031.bin",
|
| 926 |
+
"model.layers.78.self_attn.k_proj.weight": "pytorch_model-00029-of-00031.bin",
|
| 927 |
+
"model.layers.78.self_attn.o_proj.weight": "pytorch_model-00029-of-00031.bin",
|
| 928 |
+
"model.layers.78.self_attn.q_proj.bias": "pytorch_model-00029-of-00031.bin",
|
| 929 |
+
"model.layers.78.self_attn.q_proj.weight": "pytorch_model-00029-of-00031.bin",
|
| 930 |
+
"model.layers.78.self_attn.v_proj.bias": "pytorch_model-00029-of-00031.bin",
|
| 931 |
+
"model.layers.78.self_attn.v_proj.weight": "pytorch_model-00029-of-00031.bin",
|
| 932 |
+
"model.layers.79.input_layernorm.weight": "pytorch_model-00030-of-00031.bin",
|
| 933 |
+
"model.layers.79.mlp.down_proj.weight": "pytorch_model-00030-of-00031.bin",
|
| 934 |
+
"model.layers.79.mlp.gate_proj.weight": "pytorch_model-00030-of-00031.bin",
|
| 935 |
+
"model.layers.79.mlp.up_proj.weight": "pytorch_model-00030-of-00031.bin",
|
| 936 |
+
"model.layers.79.post_attention_layernorm.weight": "pytorch_model-00030-of-00031.bin",
|
| 937 |
+
"model.layers.79.self_attn.k_proj.bias": "pytorch_model-00030-of-00031.bin",
|
| 938 |
+
"model.layers.79.self_attn.k_proj.weight": "pytorch_model-00030-of-00031.bin",
|
| 939 |
+
"model.layers.79.self_attn.o_proj.weight": "pytorch_model-00030-of-00031.bin",
|
| 940 |
+
"model.layers.79.self_attn.q_proj.bias": "pytorch_model-00030-of-00031.bin",
|
| 941 |
+
"model.layers.79.self_attn.q_proj.weight": "pytorch_model-00030-of-00031.bin",
|
| 942 |
+
"model.layers.79.self_attn.v_proj.bias": "pytorch_model-00030-of-00031.bin",
|
| 943 |
+
"model.layers.79.self_attn.v_proj.weight": "pytorch_model-00030-of-00031.bin",
|
| 944 |
+
"model.layers.8.input_layernorm.weight": "pytorch_model-00004-of-00031.bin",
|
| 945 |
+
"model.layers.8.mlp.down_proj.weight": "pytorch_model-00004-of-00031.bin",
|
| 946 |
+
"model.layers.8.mlp.gate_proj.weight": "pytorch_model-00004-of-00031.bin",
|
| 947 |
+
"model.layers.8.mlp.up_proj.weight": "pytorch_model-00004-of-00031.bin",
|
| 948 |
+
"model.layers.8.post_attention_layernorm.weight": "pytorch_model-00004-of-00031.bin",
|
| 949 |
+
"model.layers.8.self_attn.k_proj.bias": "pytorch_model-00004-of-00031.bin",
|
| 950 |
+
"model.layers.8.self_attn.k_proj.weight": "pytorch_model-00004-of-00031.bin",
|
| 951 |
+
"model.layers.8.self_attn.o_proj.weight": "pytorch_model-00004-of-00031.bin",
|
| 952 |
+
"model.layers.8.self_attn.q_proj.bias": "pytorch_model-00004-of-00031.bin",
|
| 953 |
+
"model.layers.8.self_attn.q_proj.weight": "pytorch_model-00004-of-00031.bin",
|
| 954 |
+
"model.layers.8.self_attn.v_proj.bias": "pytorch_model-00004-of-00031.bin",
|
| 955 |
+
"model.layers.8.self_attn.v_proj.weight": "pytorch_model-00004-of-00031.bin",
|
| 956 |
+
"model.layers.9.input_layernorm.weight": "pytorch_model-00005-of-00031.bin",
|
| 957 |
+
"model.layers.9.mlp.down_proj.weight": "pytorch_model-00005-of-00031.bin",
|
| 958 |
+
"model.layers.9.mlp.gate_proj.weight": "pytorch_model-00004-of-00031.bin",
|
| 959 |
+
"model.layers.9.mlp.up_proj.weight": "pytorch_model-00005-of-00031.bin",
|
| 960 |
+
"model.layers.9.post_attention_layernorm.weight": "pytorch_model-00005-of-00031.bin",
|
| 961 |
+
"model.layers.9.self_attn.k_proj.bias": "pytorch_model-00004-of-00031.bin",
|
| 962 |
+
"model.layers.9.self_attn.k_proj.weight": "pytorch_model-00004-of-00031.bin",
|
| 963 |
+
"model.layers.9.self_attn.o_proj.weight": "pytorch_model-00004-of-00031.bin",
|
| 964 |
+
"model.layers.9.self_attn.q_proj.bias": "pytorch_model-00004-of-00031.bin",
|
| 965 |
+
"model.layers.9.self_attn.q_proj.weight": "pytorch_model-00004-of-00031.bin",
|
| 966 |
+
"model.layers.9.self_attn.v_proj.bias": "pytorch_model-00004-of-00031.bin",
|
| 967 |
+
"model.layers.9.self_attn.v_proj.weight": "pytorch_model-00004-of-00031.bin",
|
| 968 |
+
"model.norm.weight": "pytorch_model-00030-of-00031.bin"
|
| 969 |
+
}
|
| 970 |
+
}
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,20 @@
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|
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|
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|
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|
|
|
|
| 1 |
+
{
|
| 2 |
+
"additional_special_tokens": [
|
| 3 |
+
"<|im_start|>",
|
| 4 |
+
"<|im_end|>"
|
| 5 |
+
],
|
| 6 |
+
"eos_token": {
|
| 7 |
+
"content": "<|im_end|>",
|
| 8 |
+
"lstrip": false,
|
| 9 |
+
"normalized": false,
|
| 10 |
+
"rstrip": false,
|
| 11 |
+
"single_word": false
|
| 12 |
+
},
|
| 13 |
+
"pad_token": {
|
| 14 |
+
"content": "<|end_of_text|>",
|
| 15 |
+
"lstrip": false,
|
| 16 |
+
"normalized": false,
|
| 17 |
+
"rstrip": false,
|
| 18 |
+
"single_word": false
|
| 19 |
+
}
|
| 20 |
+
}
|
tokenizer.json
ADDED
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|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,51 @@
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|
|
|
|
|
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|
|
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|
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|
|
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|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"added_tokens_decoder": {
|
| 4 |
+
"151643": {
|
| 5 |
+
"content": "<|endoftext|>",
|
| 6 |
+
"lstrip": false,
|
| 7 |
+
"normalized": false,
|
| 8 |
+
"rstrip": false,
|
| 9 |
+
"single_word": false,
|
| 10 |
+
"special": true
|
| 11 |
+
},
|
| 12 |
+
"151644": {
|
| 13 |
+
"content": "<|im_start|>",
|
| 14 |
+
"lstrip": false,
|
| 15 |
+
"normalized": false,
|
| 16 |
+
"rstrip": false,
|
| 17 |
+
"single_word": false,
|
| 18 |
+
"special": false
|
| 19 |
+
},
|
| 20 |
+
"151645": {
|
| 21 |
+
"content": "<|im_end|>",
|
| 22 |
+
"lstrip": false,
|
| 23 |
+
"normalized": false,
|
| 24 |
+
"rstrip": false,
|
| 25 |
+
"single_word": false,
|
| 26 |
+
"special": false
|
| 27 |
+
},
|
| 28 |
+
"151646": {
|
| 29 |
+
"content": "<|end_of_text|>",
|
| 30 |
+
"lstrip": false,
|
| 31 |
+
"normalized": false,
|
| 32 |
+
"rstrip": false,
|
| 33 |
+
"single_word": false,
|
| 34 |
+
"special": true
|
| 35 |
+
}
|
| 36 |
+
},
|
| 37 |
+
"additional_special_tokens": [
|
| 38 |
+
"<|im_start|>",
|
| 39 |
+
"<|im_end|>"
|
| 40 |
+
],
|
| 41 |
+
"bos_token": null,
|
| 42 |
+
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
| 43 |
+
"clean_up_tokenization_spaces": false,
|
| 44 |
+
"eos_token": "<|im_end|>",
|
| 45 |
+
"errors": "replace",
|
| 46 |
+
"model_max_length": 131072,
|
| 47 |
+
"pad_token": "<|end_of_text|>",
|
| 48 |
+
"split_special_tokens": false,
|
| 49 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 50 |
+
"unk_token": null
|
| 51 |
+
}
|
vocab.json
ADDED
|
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|