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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+vet-qwen3b-v2-lora/tokenizer.json filter=lfs diff=lfs merge=lfs -text

vet-qwen3b-v2-lora/README.md

@@ -0,0 +1,63 @@
+---
+base_model: unsloth/Qwen2.5-3B-Instruct-bnb-4bit
+library_name: peft
+model_name: vet-qwen3b-v2
+tags:
+- base_model:adapter:unsloth/Qwen2.5-3B-Instruct-bnb-4bit
+- lora
+- sft
+- transformers
+- trl
+- unsloth
+licence: license
+pipeline_tag: text-generation
+---
+
+# Model Card for vet-qwen3b-v2
+
+This model is a fine-tuned version of [unsloth/Qwen2.5-3B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Qwen2.5-3B-Instruct-bnb-4bit).
+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="None", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+
+ 
+
+
+This model was trained with SFT.
+
+### Framework versions
+
+- PEFT 0.19.1
+- TRL: 0.24.0
+- Transformers: 5.5.0
+- Pytorch: 2.10.0
+- Datasets: 4.3.0
+- Tokenizers: 0.22.2
+
+## Citations
+
+
+
+Cite TRL as:
+    
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```

vet-qwen3b-v2-lora/adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Qwen2ForCausalLM",
+    "parent_library": "transformers.models.qwen2.modeling_qwen2",
+    "unsloth_fixed": true
+  },
+  "base_model_name_or_path": "unsloth/Qwen2.5-3B-Instruct-bnb-4bit",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "o_proj",
+    "k_proj",
+    "up_proj",
+    "gate_proj",
+    "q_proj",
+    "v_proj",
+    "down_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

vet-qwen3b-v2-lora/adapter_model.safetensors

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+size 119801528

vet-qwen3b-v2-lora/chat_template.jinja

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+{% for message in messages %}{% if message['role'] == 'user' %}{{'<|im_start|>user
+' + message['content'] + '<|im_end|>
+'}}{% elif message['role'] == 'assistant' %}{{'<|im_start|>assistant
+' + message['content'] + '<|im_end|>
+' }}{% else %}{{ '<|im_start|>system
+' + message['content'] + '<|im_end|>
+' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}

vet-qwen3b-v2-lora/tokenizer.json

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+size 11422166

vet-qwen3b-v2-lora/tokenizer_config.json

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+}

vet-qwen3b-v2-lora/train_config.json

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+{
+  "data": "v2_train_data.json",
+  "output": "./output/vet-qwen3b-v2",
+  "model": "unsloth/Qwen2.5-3B-Instruct-bnb-4bit",
+  "epochs": 2,
+  "batch_size": 2,
+  "grad_accum": 4,
+  "lr": 0.0002,
+  "lora_r": 16,
+  "lora_alpha": 32,
+  "max_seq_length": 4096,
+  "save_steps": 100,
+  "use_unsloth": true
+}