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@@ -6,20 +6,68 @@ library_name: transformers
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  pipeline_tag: text-generation
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  tags:
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  - axolotl
 
 
 
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  license: apache-2.0
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  datasets:
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  - NousResearch/Hermes-3-Dataset
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  # Qwen3-Hermes8B-v1
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- This is a merged LoRA model based on Qwen/Qwen3-8B, SFT on Hermes3 Dataset
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  ## Model Details
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  - **Base Model**: Qwen/Qwen3-8B
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  - **Language**: English (en)
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  - **Library**: transformers
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Usage
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@@ -35,14 +83,95 @@ model = AutoModelForCausalLM.from_pretrained(
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  device_map="auto"
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  )
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- # Example usage
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- text = "Hey. How are you?"
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  inputs = tokenizer(text, return_tensors="pt")
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- outputs = model.generate(**inputs, max_length=100)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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  print(response)
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  ```
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  ## Training Details
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- This model was trained using Axolotl & DeepSpeed Zero2 using 8xB200 Cluster from PrimeIntellect.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  pipeline_tag: text-generation
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  tags:
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  - axolotl
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+ - reasoning
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+ - math
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+ - commonsense
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  license: apache-2.0
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  datasets:
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  - NousResearch/Hermes-3-Dataset
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+ model-index:
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+ - name: Qwen3-Hermes8B-v1
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+ results:
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+ - task:
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+ type: text-generation
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+ name: Text Generation
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+ dataset:
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+ name: HellaSwag
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+ type: hellaswag
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+ metrics:
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+ - type: accuracy
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+ value: 0.823
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+ name: Accuracy
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+ - task:
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+ type: text-generation
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+ name: Mathematical Reasoning
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+ dataset:
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+ name: GSM8K
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+ type: gsm8k
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+ metrics:
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+ - type: accuracy
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+ value: 0.871
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+ name: Accuracy
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+ - task:
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+ type: text-generation
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+ name: Theory of Mind
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+ dataset:
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+ name: TheoryPlay
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+ type: theoryplay
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+ metrics:
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+ - type: accuracy
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+ value: 0.35
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+ name: Accuracy
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  ---
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  # Qwen3-Hermes8B-v1
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+ This is a merged LoRA model based on Qwen/Qwen3-8B, SFT on Hermes3 Dataset. The model demonstrates strong performance across reasoning, mathematical problem-solving, and commonsense understanding tasks.
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  ## Model Details
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  - **Base Model**: Qwen/Qwen3-8B
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  - **Language**: English (en)
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  - **Library**: transformers
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+ - **Training Method**: LoRA fine-tuning with Axolotl
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+ - **Infrastructure**: 8xB200 Cluster from PrimeIntellect
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+ - **Training Framework**: DeepSpeed Zero2
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+
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+ ## Performance
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+
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+ | Benchmark | Score | Description |
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+ |-----------|-------|-------------|
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+ | **HellaSwag** | 82.3% | Commonsense reasoning and natural language inference |
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+ | **GSM8K** | 87.1% | Grade school math word problems |
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+ | **TheoryPlay** | 35% | Theory of mind and social reasoning tasks |
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+
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  ## Usage
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  device_map="auto"
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  )
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+ # Example usage for reasoning tasks
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+ text = "Sarah believes that her keys are in her purse, but they are actually on the kitchen table. Where will Sarah look for her keys?"
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  inputs = tokenizer(text, return_tensors="pt")
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+ outputs = model.generate(
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+ **inputs,
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+ max_length=200,
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+ temperature=0.1,
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+ do_sample=True,
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+ pad_token_id=tokenizer.eos_token_id
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+ )
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+ response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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+ print(response)
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+ ```
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+
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+ ### Chat Format
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+
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+ This model supports the Hermes chat format:
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+
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+ ```python
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+ def format_chat(messages):
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+ formatted = ""
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+ for message in messages:
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+ role = message["role"]
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+ content = message["content"]
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+ if role == "system":
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+ formatted += f"<|im_start|>system\n{content}<|im_end|>\n"
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+ elif role == "user":
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+ formatted += f"<|im_start|>user\n{content}<|im_end|>\n"
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+ elif role == "assistant":
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+ formatted += f"<|im_start|>assistant\n{content}<|im_end|>\n"
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+ formatted += "<|im_start|>assistant\n"
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+ return formatted
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+
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+ messages = [
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+ {"role": "system", "content": "You are a helpful assistant."},
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+ {"role": "user", "content": "Solve this math problem: A store has 45 apples. If they sell 1/3 of them in the morning and 1/5 of the remaining apples in the afternoon, how many apples are left?"}
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+ ]
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+
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+ prompt = format_chat(messages)
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+ inputs = tokenizer(prompt, return_tensors="pt")
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+ outputs = model.generate(**inputs, max_length=300, temperature=0.1)
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  response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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  print(response)
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  ```
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  ## Training Details
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+ - **Training Framework**: Axolotl with DeepSpeed Zero2 optimization
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+ - **Hardware**: 8x NVIDIA B200 GPUs (PrimeIntellect cluster)
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+ - **Base Model**: Qwen/Qwen3-8B
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+ - **Training Method**: Low-Rank Adaptation (LoRA)
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+ - **Dataset**: NousResearch/Hermes-3-Dataset
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+ - **Training Duration**: 6 hours
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+ - **Learning Rate**: 0.0004
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+ - **Batch Size**: 8
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+ - **Sequence Length**: 4096
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+
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+ ## Evaluation Methodology
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+
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+ All evaluations were conducted using:
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+ - **HellaSwag**: Standard validation set with 4-way multiple choice accuracy
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+ - **GSM8K**: Test set with exact match accuracy on final numerical answers
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+ - **TheoryPlay**: Validation set with accuracy on theory of mind reasoning tasks
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+
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+ ## Limitations
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+
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+ - The model may still struggle with very complex mathematical proofs
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+ - Performance on non-English languages may be limited
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+ - May occasionally generate inconsistent responses in edge cases
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+ - Training data cutoff affects knowledge of recent events
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+
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+ ## Ethical Considerations
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+
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+ This model has been trained on curated datasets and should be used responsibly. Users should:
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+ - Verify important information from the model
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+ - Be aware of potential biases in training data
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+ - Use appropriate content filtering for production applications
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @misc{qwen3-hermes8b-v1,
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+ title={Qwen3-Hermes8B-v1: A Fine-tuned Language Model for Reasoning Tasks},
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+ author={[Your Name]},
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+ year={2025},
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+ url={https://huggingface.co/justinj92/Qwen3-Hermes8B-v1}
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+ }
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+ ```
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+
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+ ## License
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+
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+ This model is released under the Apache 2.0 license.