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Model: prithivMLmods/Sombrero-Opus-14B-Elite13 Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model:
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- prithivMLmods/Sombrero-Opus-14B-Elite6
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pipeline_tag: text-generation
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tags:
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- SFT
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- text-generation-inference
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- abliterated
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- trl
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- code
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- moderately abliterated
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- math
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language:
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- en
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library_name: transformers
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---
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# **Sombrero-Opus-14B-Elite13**
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> Sombrero-Opus-14B-Elite13 builds upon the Qwen 2.5 14B modality architecture, elevating reasoning performance in mid- to large-scale models. This iteration focuses on enhancing general-purpose comprehension, structured intelligence, and interactive versatility. Fine-tuned with an advanced reasoning chain and carefully curated datasets, Elite13 offers improved contextual understanding, logical coherence, and multi-step problem-solving.
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Key improvements include:
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1. **Expanded Domain Fluency**: Delivers refined general knowledge across disciplines for more accurate and coherent answers.
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2. **Advanced Instruction Parsing**: Enhanced capacity to interpret and execute complex instructions while preserving structure and clarity.
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3. **Robust Prompt Flexibility**: Excels in adapting to diverse interaction styles, from casual inquiries to formal requests.
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4. **Extended Context Window**: Handles up to 128K tokens of input and generates up to 8K tokens in a single output — ideal for detailed reasoning and expansive replies.
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5. **Global Linguistic Range**: Offers proficiency in 29+ languages, including English, Chinese, French, Spanish, Japanese, Arabic, and more.
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---
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# **Quickstart with Transformers**
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Use the following snippet to load and test the model using `transformers` and `apply_chat_template`:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Sombrero-Opus-14B-Elite13"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "What are the key principles of general-purpose AI?"
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messages = [
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{"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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---
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# **Intended Use**
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1. **Cognitive Reasoning & General Q\&A**
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Designed to support high-level thinking and accurate responses across general domains.
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2. **Education & Research Support**
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Suitable for generating study guides, academic summaries, and informative explanations.
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3. **Conversational Intelligence**
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Powers AI assistants and chatbots with memory-aware, context-sensitive dialogues.
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4. **Cross-Language Communication**
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Useful in multilingual environments for translation, communication, and content creation.
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5. **Data-Aware Structuring**
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Capable of converting unstructured data into meaningful formats like JSON or tabular summaries.
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6. **Lengthy Content Generation**
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Suitable for drafting articles, technical documents, or creative prose with sustained coherence.
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---
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# **Limitations**
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1. **Resource-Intensive Execution**
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Requires robust computational infrastructure (e.g., ≥48GB VRAM) to run efficiently.
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2. **Residual Biases**
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Though tuned for neutrality, occasional bias may surface from inherited training data.
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3. **Creative Variability**
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Creative outputs such as fiction or poetry may vary in quality and style coherence.
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4. **Lack of Real-Time Knowledge**
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The model operates with a static knowledge base and lacks access to current world events.
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5. **Drift in Extended Outputs**
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Long responses may introduce cumulative inaccuracies or lose focus over time.
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6. **Prompt Dependence**
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Output quality is sensitive to the clarity and specificity of the initial prompt.
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added_tokens.json
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config.json
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config.json
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{
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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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"hidden_act": "silu",
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"initializer_range": 0.02,
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"intermediate_size": 13824,
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"max_position_embeddings": 32768,
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"max_window_layers": 70,
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"model_type": "qwen2",
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"num_attention_heads": 40,
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"num_hidden_layers": 48,
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"num_key_value_heads": 8,
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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"top_k": 20,
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"transformers_version": "4.51.3"
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207
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|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|im_end|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user