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Model: prithivMLmods/Sombrero-R1-14B-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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language:
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- en
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base_model:
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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- math
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- moderately abliterated
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- abliterated
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- code
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- R1
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- RL
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---
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# **Sombrero-R1-14B-Elite13**
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> Sombrero-R1-14B-Elite13 is a fine-tuned variant of the DeepSeek-R1-Distill-Qwen-14B model, enhanced through reinforcement learning to serve as a high-performance reasoning assistant. It excels in both mathematical problem-solving and general-purpose conversational tasks. This model combines distilled efficiency with refined instruction-following behavior, offering an ideal balance of speed, capability, and coherence for complex interactive tasks.
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### Key Enhancements
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1. **Reinforcement Learning Fine-Tuning**
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Trained with reinforcement learning objectives to optimize for alignment, reward-guided reasoning, and helpfulness in conversation.
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2. **Mathematical Reasoning Proficiency**
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Delivers accurate solutions and step-by-step breakdowns for algebra, calculus, number theory, logic puzzles, and applied mathematics.
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3. **Instruction Adherence**
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Capable of understanding and following multi-part instructions, including structured tasks and iterative refinement prompts.
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4. **Expanded Context Handling**
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Supports up to 128K tokens of context with output lengths up to 8K tokens, ideal for technical and educational use cases.
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5. **Cross-Domain Knowledge**
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Offers broad general knowledge capabilities, making it suitable for tutoring, research, and exploratory conversation across topics.
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---
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# **Quickstart with Transformers**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Sombrero-R1-14B-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 = "Solve: Integrate (x^2 * e^x) dx"
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messages = [
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{"role": "system", "content": "You are a helpful AI assistant skilled in math and reasoning."},
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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 Cases**
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1. **Mathematics Problem Solving**
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Ideal for step-by-step derivations, symbolic computation, numerical explanations, and LaTeX-supported outputs.
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2. **Educational and Instructional Support**
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Helpful in classrooms and learning platforms, offering guided explanations for students and instructors.
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3. **Chat-based Reasoning**
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Designed for coherent, context-aware dialogue generation with structured logic and continuity.
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4. **Multilingual Knowledge Assistance**
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Supports 29+ languages, including English, Chinese, French, German, Arabic, and others, for multilingual learning.
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5. **Document and Code Explanation**
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Can explain complex documents, code snippets, or structured logic flows in natural language.
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---
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# **Known Limitations**
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1. **Compute Intensive**
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Requires high-memory hardware (e.g., ≥48GB VRAM) to fully utilize context length and generation capacity.
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2. **Potential for Bias and Hallucinations**
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While tuned for alignment, some responses may still exhibit artifacts from pretraining biases or inaccuracies in edge cases.
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3. **Drift in Long Responses**
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Output may occasionally degrade in structure or accuracy across long generations.
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4. **Static Knowledge**
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Does not have real-time awareness or access to events or research developments post-training.
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5. **Creative Task Variability**
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While optimized for logic, its performance in narrative or subjective content may be inconsistent.
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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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"bos_token_id": 151643,
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"eos_token_id": 151643,
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"hidden_act": "silu",
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"hidden_size": 5120,
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"initializer_range": 0.02,
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"intermediate_size": 13824,
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"max_position_embeddings": 131072,
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"max_window_layers": 48,
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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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"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": "4.51.3",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 152064
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}
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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"temperature": 0.6,
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"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"bos_token": "<｜begin▁of▁sentence｜>",
|
||||
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>'}}{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<｜end▁of▁sentence｜>",
|
||||
"extra_special_tokens": {},
|
||||
"legacy": true,
|
||||
"model_max_length": 16384,
|
||||
"pad_token": "<｜end▁of▁sentence｜>",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": null,
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user