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Model: mnoukhov/nuevamol-135m-reinvent-sft
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: mnoukhov/nuevamol-135m-6B-wd3
+library_name: transformers
+model_name: nuevamol-135m-reinvent-sft
+tags:
+- generated_from_trainer
+- sft
+- trl
+licence: license
+---
+
+# Model Card for nuevamol-135m-reinvent-sft
+
+This model is a fine-tuned version of [mnoukhov/nuevamol-135m-6B-wd3](https://huggingface.co/mnoukhov/nuevamol-135m-6B-wd3).
+It has been trained using [TRL](https://github.com/huggingface/trl).
+
+## Quick start
+
+```python
+from transformers import pipeline
+
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="mnoukhov/nuevamol-135m-reinvent-sft", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/diegos-mila/chem-llm-pipeline/runs/ysk1thks) 
+
+
+
+This model was trained with SFT.
+
+### Framework versions
+
+- TRL: 1.5.1
+- Transformers: 5.3.0
+- Pytorch: 2.9.0
+- Datasets: 4.8.5
+- Tokenizers: 0.22.1
+
+## Citations
+
+
+
+Cite TRL as:
+    
+```bibtex
+@software{vonwerra2020trl,
+  title   = {{TRL: Transformers Reinforcement Learning}},
+  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
+  license = {Apache-2.0},
+  url     = {https://github.com/huggingface/trl},
+  year    = {2020}
+}
+```

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 389,
+  "dtype": "bfloat16",
+  "eos_token_id": 388,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 576,
+  "initializer_range": 0.041666666666666664,
+  "intermediate_size": 1536,
+  "is_llama_config": true,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 9,
+  "num_hidden_layers": 30,
+  "num_key_value_heads": 3,
+  "pad_token_id": 387,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_interleaved": false,
+  "rope_parameters": {
+    "rope_theta": 100000,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.3.0",
+  "use_cache": false,
+  "vocab_size": 391
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 389,
+  "eos_token_id": [
+    388
+  ],
+  "pad_token_id": 387,
+  "transformers_version": "5.3.0"
+}

model.safetensors

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tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "[BOS]",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "[EOS]",
+  "is_local": false,
+  "model_max_length": 128,
+  "pad_token": "[PAD]",
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "[UNK]"
+}

training_args.bin

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wandb_run_info.json

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+{"run_id": "ysk1thks", "run_name": "nuevamol-135m-reinvent-sft", "project": "chem-llm-pipeline"}