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Model: Rumiii/LlamaTron-RS1-Nemesis-1B
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+datasets:
+- OpenMed/Medical-Reasoning-SFT-MiniMax-M2.1
+base_model:
+- meta-llama/Llama-3.2-1B-Instruct
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- medical
+- clinical
+- reasoning
+- qlora
+- llama
+- healthcare
+- chain-of-thought
+---
+
+# LlamaTron RS1 Nemesis 1B
+
+**Base Model:** meta-llama/Llama-3.2-1B-Instruct
+**Dataset:** OpenMed/Medical-Reasoning-SFT-MiniMax-M2.1
+
+---
+
+## Model Overview
+
+LlamaTron RS1 Nemesis is a medical reasoning model produced by fine-tuning meta-llama/Llama-3.2-1B-Instruct on the Medical-Reasoning-SFT-MiniMax-M2.1 dataset using QLoRA. The dataset contains 204,773 clinical reasoning conversations with full chain-of-thought traces covering differential diagnosis, treatment planning, pharmacology, and clinical case analysis.
+
+Despite being a 1 billion parameter model, it handles complex clinical questions with structured and coherent reasoning.
+
+---
+
+## Demo Screenshots
+
+
+## Info
+
+
+![y3msQ](https://cdn-uploads.huggingface.co/production/uploads/66e00ba55e4fd4bfead4a97c/W2xJUdRLD2Y_3RIPTFndV.jpeg)
+
+
+### Interface
+
+
+![1](https://cdn-uploads.huggingface.co/production/uploads/66e00ba55e4fd4bfead4a97c/rrn4HJxbXS5wd8FUuHica.png)
+
+
+
+### Model Response Example
+
+
+![2](https://cdn-uploads.huggingface.co/production/uploads/66e00ba55e4fd4bfead4a97c/XQjsOB6hNsBpb01naguAg.png)
+
+---
+
+## Training Setup
+
+| Parameter | Value |
+|-----------|-------|
+| Base Model | meta-llama/Llama-3.2-1B-Instruct |
+| GPU | NVIDIA H200 |
+| Method | QLoRA (4-bit NF4 + LoRA) |
+| LoRA Rank | r=8, alpha=16 |
+| LoRA Target Modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| LoRA Dropout | 0.05 |
+| Trainable Parameters | 5.6M out of 1.24B (0.45%) |
+| Effective Batch Size | 32 (8 per device x 4 gradient accumulation) |
+| Learning Rate | 2e-4 |
+| LR Scheduler | Cosine |
+| Warmup Ratio | 0.05 |
+| Optimizer | paged_adamw_8bit |
+| Max Sequence Length | 512 |
+| Precision | bf16 + tf32 |
+| Epochs | 1 |
+| Total Steps | 6,271 |
+| Training Time | 3 hours 59 minutes |
+
+---
+
+## Training Results
+
+| Step | Train Loss | Validation Loss |
+|------|------------|-----------------|
+| 500 | 1.5759 | 1.6126 |
+| 1000 | 1.5176 | 1.5538 |
+| 1500 | 1.4805 | 1.5256 |
+| 2000 | 1.4795 | 1.5060 |
+| 2500 | 1.4508 | 1.4939 |
+| 3000 | 1.4534 | 1.4815 |
+| 3500 | 1.4384 | 1.4739 |
+| 4000 | 1.4228 | 1.4663 |
+| 4500 | 1.4251 | 1.4605 |
+| 5000 | 1.4301 | 1.4567 |
+| 5500 | 1.4102 | 1.4545 |
+| 6000 | 1.4246 | 1.4538 |
+| 6271 | 1.4200 | 1.4500 |
+
+Loss decreased consistently across all steps with train and validation loss tracking closely. No overfitting observed.
+
+---
+
+## Dataset
+
+Trained on [Medical-Reasoning-SFT-MiniMax-M2.1](https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-MiniMax-M2.1) released by [Maziyar Panahi](https://huggingface.co/maziarpanahi) under the OpenMed initiative.
+
+| Property | Value |
+|----------|-------|
+| Total Samples | 204,773 |
+| Estimated Tokens | ~621 Million |
+| Format | Multi-turn chat with chain-of-thought reasoning |
+| License | Apache 2.0 |
+| Topics | Differential diagnosis, treatment planning, pharmacology, clinical case analysis |
+
+---
+
+## How to Use
+
+### Load the Model
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+model_id = "Rumiii/LlamaTron_RS1_Nemesis_1B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+messages = [
+    {
+        "role": "system",
+        "content": "You are LlamaTron RS1 Nemesis, a knowledgeable and compassionate medical AI assistant. Provide accurate, evidence-based medical information clearly and helpfully."
+    },
+    {
+        "role": "user",
+        "content": "What are the early symptoms of Type 2 Diabetes?"
+    },
+]
+
+output = pipe(
+    messages,
+    max_new_tokens=400,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+)
+
+print(output[0]["generated_text"][-1]["content"])
+```
+
+---
+
+## Repository
+
+The full training code, merging scripts, and inference interface are available on GitHub:
+[github.com/sufirumii/LlamaTron-RS1-Nemesis-1B](https://github.com/sufirumii/LlamaTron-RS1-Nemesis-1B)
+
+### GitHub
+
+
+![Sample](https://cdn-uploads.huggingface.co/production/uploads/66e00ba55e4fd4bfead4a97c/1YP1OHz_S5fvzCwFTkWrm.png)
+
+---
+
+## Limitations
+
+- This model is intended for research and educational purposes only
+- It is not a substitute for professional medical advice, diagnosis, or treatment
+- The model was trained with a maximum sequence length of 512 tokens which may limit performance on longer clinical texts
+- Always consult a qualified healthcare provider for medical decisions
+
+---
+
+## Credits
+
+- **Dataset:** [Maziyar Panahi](https://huggingface.co/maziarpanahi) and the [OpenMed](https://huggingface.co/OpenMed) initiative for releasing the Medical-Reasoning-SFT-MiniMax-M2.1 dataset under Apache 2.0
+- **Base Model:** Meta AI for releasing Llama-3.2-1B-Instruct
+- **Libraries:** Hugging Face Transformers, PEFT, TRL, BitsAndBytes, Accelerate
+
+---
+
+## License
+
+Apache 2.0 — see [LICENSE](LICENSE) for details.

config.json

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+{
+  "_name_or_path": "meta-llama/Llama-3.2-1B-Instruct",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.44.0",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.44.0"
+}

model.safetensors

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+size 2471645464

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}