Text Generation
PEFT
Safetensors
Transformers
lora
sft
trl
sampled_response
cluster_0
conversational
Instructions to use 1jamesthompson1/Qwen3.5-9B-nz-wvs-sampled_response-cluster_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use 1jamesthompson1/Qwen3.5-9B-nz-wvs-sampled_response-cluster_0 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B") model = PeftModel.from_pretrained(base_model, "1jamesthompson1/Qwen3.5-9B-nz-wvs-sampled_response-cluster_0") - Transformers
How to use 1jamesthompson1/Qwen3.5-9B-nz-wvs-sampled_response-cluster_0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="1jamesthompson1/Qwen3.5-9B-nz-wvs-sampled_response-cluster_0") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("1jamesthompson1/Qwen3.5-9B-nz-wvs-sampled_response-cluster_0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use 1jamesthompson1/Qwen3.5-9B-nz-wvs-sampled_response-cluster_0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "1jamesthompson1/Qwen3.5-9B-nz-wvs-sampled_response-cluster_0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "1jamesthompson1/Qwen3.5-9B-nz-wvs-sampled_response-cluster_0", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/1jamesthompson1/Qwen3.5-9B-nz-wvs-sampled_response-cluster_0
- SGLang
How to use 1jamesthompson1/Qwen3.5-9B-nz-wvs-sampled_response-cluster_0 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "1jamesthompson1/Qwen3.5-9B-nz-wvs-sampled_response-cluster_0" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "1jamesthompson1/Qwen3.5-9B-nz-wvs-sampled_response-cluster_0", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "1jamesthompson1/Qwen3.5-9B-nz-wvs-sampled_response-cluster_0" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "1jamesthompson1/Qwen3.5-9B-nz-wvs-sampled_response-cluster_0", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use 1jamesthompson1/Qwen3.5-9B-nz-wvs-sampled_response-cluster_0 with Docker Model Runner:
docker model run hf.co/1jamesthompson1/Qwen3.5-9B-nz-wvs-sampled_response-cluster_0
Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +149 -0
- adapter_config.json +50 -0
- adapter_model.safetensors +3 -0
- chat_template.jinja +154 -0
- environment.json +82 -0
- finetune_config.json +25 -0
- processor_config.json +60 -0
- tokenizer.json +3 -0
- tokenizer_config.json +32 -0
- training.log +16 -0
- training_args.bin +3 -0
- training_log.json +484 -0
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| 1 |
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---
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base_model: Qwen/Qwen3.5-9B
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library_name: peft
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model_name: cluster_0
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tags:
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- base_model:adapter:Qwen/Qwen3.5-9B
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- lora
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- sft
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| 9 |
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- transformers
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| 10 |
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- trl
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- sampled_response
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- cluster_0
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license: cc-by-sa-4.0
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pipeline_tag: text-generation
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datasets:
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- 1jamesthompson1/wvs-nz-value-alignment
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| 17 |
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---
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| 18 |
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| 19 |
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+
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| 23 |
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# Qwen3.5-9B LoRA — Sampled Response, Cluster 0
|
| 24 |
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This model is a LoRA fine-tune of [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) as part of the
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[AIML589 project](https://github.com/1jamesthompson1/AIML589).
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This adapter is licensed under CC BY-SA 4.0.
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| 29 |
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| 30 |
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## Dataset
|
| 31 |
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Fine-tuned on the **sampled_response** config of the
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[wvs-nz-value-alignment](https://huggingface.co/datasets/1jamesthompson1/wvs-nz-value-alignment)
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dataset, **cluster_0** subpopulation.
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Part of the [wvs-nz-value-alignment](https://huggingface.co/collections/wvs-nz-value-alignment) collection.
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**GPU:** NVIDIA RTX 6000 Ada Generation · **Training time:** 18m 22s
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| 41 |
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## Training hyperparameters
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| 44 |
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| 45 |
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| Parameter | Value |
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| 46 |
+
|-----------|-------|
|
| 47 |
+
| LoRA rank | 16 |
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| 48 |
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| LoRA alpha | 32 |
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| 49 |
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| LoRA dropout | 0.05 |
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| 50 |
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| DoRA | False |
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| 51 |
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| Learning rate | 0.0002 |
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| 52 |
+
| Batch size | 4 |
|
| 53 |
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| Gradient accumulation | 4 |
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| 54 |
+
| Epochs | 3 |
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| 55 |
+
| Max seq length | 1024 |
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| 56 |
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| Warmup ratio | 0.1 |
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| 57 |
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| Dtype | bf16 |
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| 59 |
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## Training log
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| 60 |
+
|
| 61 |
+
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| 62 |
+
```
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| 63 |
+
{"loss": 4.099612808227539, "grad_norm": 3.3916518688201904, "learning_rate": 7.82608695652174e-05, "entropy": 1.0828558593988418, "mean_token_accuracy": 0.44957960266619923, "num_tokens": 24658.0, "epoch": 0.13245033112582782, "step": 10}
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| 64 |
+
{"eval_loss": 0.5499903559684753, "eval_runtime": 9.3737, "eval_samples_per_second": 16.002, "eval_steps_per_second": 4.054, "eval_entropy": 0.5737218025483584, "eval_mean_token_accuracy": 0.7952850923726433, "eval_num_tokens": 24658.0, "epoch": 0.13245033112582782, "step": 10}
|
| 65 |
+
{"loss": 0.4331226825714111, "grad_norm": 3.621782064437866, "learning_rate": 0.00016521739130434784, "entropy": 0.4598923083394766, "mean_token_accuracy": 0.8161924198269844, "num_tokens": 48994.0, "epoch": 0.26490066225165565, "step": 20}
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| 66 |
+
{"eval_loss": 0.5726626515388489, "eval_runtime": 9.3836, "eval_samples_per_second": 15.985, "eval_steps_per_second": 4.05, "eval_entropy": 0.3104277225701432, "eval_mean_token_accuracy": 0.8032894793309664, "eval_num_tokens": 48994.0, "epoch": 0.26490066225165565, "step": 20}
|
| 67 |
+
{"loss": 0.37956321239471436, "grad_norm": 3.6681301593780518, "learning_rate": 0.00019414634146341464, "entropy": 0.35528335012495516, "mean_token_accuracy": 0.8529162302613258, "num_tokens": 73216.0, "epoch": 0.3973509933774834, "step": 30}
|
| 68 |
+
{"eval_loss": 0.4902452528476715, "eval_runtime": 9.6515, "eval_samples_per_second": 15.542, "eval_steps_per_second": 3.937, "eval_entropy": 0.3591178828164151, "eval_mean_token_accuracy": 0.8244517586733165, "eval_num_tokens": 73216.0, "epoch": 0.3973509933774834, "step": 30}
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| 69 |
+
{"loss": 0.3535017728805542, "grad_norm": 1.529909372329712, "learning_rate": 0.00018439024390243903, "entropy": 0.31258961334824564, "mean_token_accuracy": 0.8600528433918952, "num_tokens": 97165.0, "epoch": 0.5298013245033113, "step": 40}
|
| 70 |
+
{"eval_loss": 0.5773032903671265, "eval_runtime": 8.8699, "eval_samples_per_second": 16.911, "eval_steps_per_second": 4.284, "eval_entropy": 0.40491867006609317, "eval_mean_token_accuracy": 0.7638627878929439, "eval_num_tokens": 97165.0, "epoch": 0.5298013245033113, "step": 40}
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| 71 |
+
{"loss": 0.29483020305633545, "grad_norm": 1.4113677740097046, "learning_rate": 0.00017463414634146342, "entropy": 0.3084935720078647, "mean_token_accuracy": 0.8778989180922508, "num_tokens": 121376.0, "epoch": 0.6622516556291391, "step": 50}
|
| 72 |
+
{"eval_loss": 0.5696188807487488, "eval_runtime": 8.921, "eval_samples_per_second": 16.814, "eval_steps_per_second": 4.26, "eval_entropy": 0.29807096403582317, "eval_mean_token_accuracy": 0.8039630376978925, "eval_num_tokens": 121376.0, "epoch": 0.6622516556291391, "step": 50}
|
| 73 |
+
{"loss": 0.26492722034454347, "grad_norm": 2.0661537647247314, "learning_rate": 0.00016487804878048782, "entropy": 0.268452774733305, "mean_token_accuracy": 0.8974904179573059, "num_tokens": 145557.0, "epoch": 0.7947019867549668, "step": 60}
|
| 74 |
+
{"eval_loss": 0.8559898138046265, "eval_runtime": 8.9149, "eval_samples_per_second": 16.826, "eval_steps_per_second": 4.263, "eval_entropy": 0.2022110644458352, "eval_mean_token_accuracy": 0.8000626611082178, "eval_num_tokens": 145557.0, "epoch": 0.7947019867549668, "step": 60}
|
| 75 |
+
{"loss": 0.18335200548171998, "grad_norm": 3.771090269088745, "learning_rate": 0.0001551219512195122, "entropy": 0.12369259365368634, "mean_token_accuracy": 0.9281293749809265, "num_tokens": 170605.0, "epoch": 0.9271523178807947, "step": 70}
|
| 76 |
+
{"eval_loss": 0.8269464373588562, "eval_runtime": 8.912, "eval_samples_per_second": 16.831, "eval_steps_per_second": 4.264, "eval_entropy": 0.23079313014290834, "eval_mean_token_accuracy": 0.7807487523869464, "eval_num_tokens": 170605.0, "epoch": 0.9271523178807947, "step": 70}
|
| 77 |
+
{"loss": 0.18180986642837524, "grad_norm": 2.6767499446868896, "learning_rate": 0.0001453658536585366, "entropy": 0.16967114948324466, "mean_token_accuracy": 0.9279846925484506, "num_tokens": 193719.0, "epoch": 1.0529801324503312, "step": 80}
|
| 78 |
+
{"eval_loss": 0.8880820870399475, "eval_runtime": 8.8086, "eval_samples_per_second": 17.029, "eval_steps_per_second": 4.314, "eval_entropy": 0.2405679833119441, "eval_mean_token_accuracy": 0.7817669220660862, "eval_num_tokens": 193719.0, "epoch": 1.0529801324503312, "step": 80}
|
| 79 |
+
{"loss": 0.11491576433181763, "grad_norm": 0.69949871301651, "learning_rate": 0.000135609756097561, "entropy": 0.14066557474434377, "mean_token_accuracy": 0.9485534891486168, "num_tokens": 218542.0, "epoch": 1.185430463576159, "step": 90}
|
| 80 |
+
{"eval_loss": 0.7975587844848633, "eval_runtime": 8.8612, "eval_samples_per_second": 16.928, "eval_steps_per_second": 4.288, "eval_entropy": 0.19119705641826026, "eval_mean_token_accuracy": 0.7988721861651069, "eval_num_tokens": 218542.0, "epoch": 1.185430463576159, "step": 90}
|
| 81 |
+
{"loss": 0.08289253115653991, "grad_norm": 2.014707565307617, "learning_rate": 0.00012585365853658536, "entropy": 0.05604892138508148, "mean_token_accuracy": 0.9736187428236007, "num_tokens": 243302.0, "epoch": 1.3178807947019868, "step": 100}
|
| 82 |
+
{"eval_loss": 1.1870805025100708, "eval_runtime": 8.8416, "eval_samples_per_second": 16.965, "eval_steps_per_second": 4.298, "eval_entropy": 0.114802976807512, "eval_mean_token_accuracy": 0.8206453684129213, "eval_num_tokens": 243302.0, "epoch": 1.3178807947019868, "step": 100}
|
| 83 |
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|
| 84 |
+
{"eval_loss": 0.8791955709457397, "eval_runtime": 8.8863, "eval_samples_per_second": 16.88, "eval_steps_per_second": 4.276, "eval_entropy": 0.17355985742515737, "eval_mean_token_accuracy": 0.8070958703756332, "eval_num_tokens": 267729.0, "epoch": 1.4503311258278146, "step": 110}
|
| 85 |
+
{"loss": 0.049860316514968875, "grad_norm": 0.678949773311615, "learning_rate": 0.00010634146341463416, "entropy": 0.07773116882308387, "mean_token_accuracy": 0.9833469331264496, "num_tokens": 292279.0, "epoch": 1.5827814569536423, "step": 120}
|
| 86 |
+
{"eval_loss": 1.1025115251541138, "eval_runtime": 8.9449, "eval_samples_per_second": 16.769, "eval_steps_per_second": 4.248, "eval_entropy": 0.1594012568683339, "eval_mean_token_accuracy": 0.7980889779956717, "eval_num_tokens": 292279.0, "epoch": 1.5827814569536423, "step": 120}
|
| 87 |
+
{"loss": 0.07376678586006165, "grad_norm": 2.6976561546325684, "learning_rate": 9.658536585365854e-05, "entropy": 0.0441209072858328, "mean_token_accuracy": 0.9765854984521866, "num_tokens": 316471.0, "epoch": 1.7152317880794703, "step": 130}
|
| 88 |
+
{"eval_loss": 1.2516794204711914, "eval_runtime": 8.9032, "eval_samples_per_second": 16.848, "eval_steps_per_second": 4.268, "eval_entropy": 0.12382908019782535, "eval_mean_token_accuracy": 0.770441735261365, "eval_num_tokens": 316471.0, "epoch": 1.7152317880794703, "step": 130}
|
| 89 |
+
{"loss": 0.02282872200012207, "grad_norm": 0.8916032314300537, "learning_rate": 8.682926829268293e-05, "entropy": 0.0408805181941716, "mean_token_accuracy": 0.9929233521223069, "num_tokens": 340856.0, "epoch": 1.847682119205298, "step": 140}
|
| 90 |
+
{"eval_loss": 1.273831844329834, "eval_runtime": 8.873, "eval_samples_per_second": 16.905, "eval_steps_per_second": 4.283, "eval_entropy": 0.11421615798501175, "eval_mean_token_accuracy": 0.7660557698262366, "eval_num_tokens": 340856.0, "epoch": 1.847682119205298, "step": 140}
|
| 91 |
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{"loss": 0.020523886382579803, "grad_norm": 2.7703590393066406, "learning_rate": 7.707317073170732e-05, "entropy": 0.028159727145248326, "mean_token_accuracy": 0.9912698417901993, "num_tokens": 364928.0, "epoch": 1.980132450331126, "step": 150}
|
| 92 |
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{"eval_loss": 1.5218627452850342, "eval_runtime": 8.8524, "eval_samples_per_second": 16.945, "eval_steps_per_second": 4.293, "eval_entropy": 0.09264942740800937, "eval_mean_token_accuracy": 0.7557487519163835, "eval_num_tokens": 364928.0, "epoch": 1.980132450331126, "step": 150}
|
| 93 |
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{"loss": 0.015184570848941804, "grad_norm": 0.04658397287130356, "learning_rate": 6.731707317073171e-05, "entropy": 0.016722216987737307, "mean_token_accuracy": 0.9981203000796469, "num_tokens": 387571.0, "epoch": 2.1059602649006623, "step": 160}
|
| 94 |
+
{"eval_loss": 1.6605678796768188, "eval_runtime": 8.8568, "eval_samples_per_second": 16.936, "eval_steps_per_second": 4.29, "eval_entropy": 0.07781053681920687, "eval_mean_token_accuracy": 0.770441735261365, "eval_num_tokens": 387571.0, "epoch": 2.1059602649006623, "step": 160}
|
| 95 |
+
{"loss": 0.006011705845594406, "grad_norm": 0.013418244197964668, "learning_rate": 5.756097560975609e-05, "entropy": 0.007514824125610176, "mean_token_accuracy": 0.9968627452850342, "num_tokens": 412476.0, "epoch": 2.23841059602649, "step": 170}
|
| 96 |
+
{"eval_loss": 1.763867974281311, "eval_runtime": 8.8357, "eval_samples_per_second": 16.977, "eval_steps_per_second": 4.301, "eval_entropy": 0.07184799665475639, "eval_mean_token_accuracy": 0.7737312089455756, "eval_num_tokens": 412476.0, "epoch": 2.23841059602649, "step": 170}
|
| 97 |
+
{"loss": 0.004858419671654702, "grad_norm": 0.14898061752319336, "learning_rate": 4.7804878048780485e-05, "entropy": 0.004944811350287637, "mean_token_accuracy": 0.9980769231915474, "num_tokens": 436969.0, "epoch": 2.370860927152318, "step": 180}
|
| 98 |
+
{"eval_loss": 1.827174425125122, "eval_runtime": 8.8372, "eval_samples_per_second": 16.974, "eval_steps_per_second": 4.3, "eval_entropy": 0.06969891959229962, "eval_mean_token_accuracy": 0.7720864721034703, "eval_num_tokens": 436969.0, "epoch": 2.370860927152318, "step": 180}
|
| 99 |
+
{"loss": 0.005421416833996773, "grad_norm": 0.08094702661037445, "learning_rate": 3.804878048780488e-05, "entropy": 0.004511333805567119, "mean_token_accuracy": 0.9966666668653488, "num_tokens": 461400.0, "epoch": 2.5033112582781456, "step": 190}
|
| 100 |
+
{"eval_loss": 1.859080195426941, "eval_runtime": 8.9117, "eval_samples_per_second": 16.832, "eval_steps_per_second": 4.264, "eval_entropy": 0.06829966303944514, "eval_mean_token_accuracy": 0.7753759457876808, "eval_num_tokens": 461400.0, "epoch": 2.5033112582781456, "step": 190}
|
| 101 |
+
{"loss": 0.005886765569448471, "grad_norm": 0.007371403276920319, "learning_rate": 2.8292682926829267e-05, "entropy": 0.006164468544739066, "mean_token_accuracy": 0.9951255336403847, "num_tokens": 485443.0, "epoch": 2.6357615894039736, "step": 200}
|
| 102 |
+
{"eval_loss": 1.8880680799484253, "eval_runtime": 8.8924, "eval_samples_per_second": 16.868, "eval_steps_per_second": 4.273, "eval_entropy": 0.06679329828732175, "eval_mean_token_accuracy": 0.7753759457876808, "eval_num_tokens": 485443.0, "epoch": 2.6357615894039736, "step": 200}
|
| 103 |
+
{"loss": 0.0009710748679935932, "grad_norm": 0.0006506519857794046, "learning_rate": 1.853658536585366e-05, "entropy": 0.002073692464909982, "mean_token_accuracy": 1.0, "num_tokens": 509990.0, "epoch": 2.7682119205298013, "step": 210}
|
| 104 |
+
{"eval_loss": 1.9050503969192505, "eval_runtime": 8.8469, "eval_samples_per_second": 16.955, "eval_steps_per_second": 4.295, "eval_entropy": 0.06654788765100468, "eval_mean_token_accuracy": 0.7737312089455756, "eval_num_tokens": 509990.0, "epoch": 2.7682119205298013, "step": 210}
|
| 105 |
+
{"loss": 0.01160859763622284, "grad_norm": 0.004122971091419458, "learning_rate": 8.780487804878048e-06, "entropy": 0.00959118189784931, "mean_token_accuracy": 0.9908364906907081, "num_tokens": 534335.0, "epoch": 2.9006622516556293, "step": 220}
|
| 106 |
+
{"eval_loss": 1.9109818935394287, "eval_runtime": 8.8226, "eval_samples_per_second": 17.002, "eval_steps_per_second": 4.307, "eval_entropy": 0.06608304418458781, "eval_mean_token_accuracy": 0.7737312089455756, "eval_num_tokens": 534335.0, "epoch": 2.9006622516556293, "step": 220}
|
| 107 |
+
{"eval_loss": 1.9071061611175537, "eval_runtime": 8.9501, "eval_samples_per_second": 16.76, "eval_steps_per_second": 4.246, "eval_entropy": 0.06653332771269274, "eval_mean_token_accuracy": 0.7737312089455756, "eval_num_tokens": 552465.0, "epoch": 3.0, "step": 228}
|
| 108 |
+
{"train_runtime": 1102.1217, "train_samples_per_second": 3.283, "train_steps_per_second": 0.207, "total_flos": 3.178328110793933e+16, "train_loss": 0.2925478722695915, "epoch": 3.0, "step": 228}
|
| 109 |
+
```
|
| 110 |
+
|
| 111 |
+
|
| 112 |
+
## Environment
|
| 113 |
+
|
| 114 |
+
| Package | Version |
|
| 115 |
+
|---------|---------|
|
| 116 |
+
| torch | 2.13.0 |
|
| 117 |
+
| transformers | 5.14.1 |
|
| 118 |
+
| trl | 1.9.2 |
|
| 119 |
+
| datasets | 5.0.1 |
|
| 120 |
+
| accelerate | 1.14.0 |
|
| 121 |
+
| python-dotenv | 1.2.2 |
|
| 122 |
+
| peft | 0.20.0 |
|
| 123 |
+
| bitsandbytes | 0.50.0 |
|
| 124 |
+
| huggingface-hub | ? |
|
| 125 |
+
| jinja2 | ? |
|
| 126 |
+
| torchvision | 0.28.0 |
|
| 127 |
+
| pillow | 12.3.0 |
|
| 128 |
+
|
| 129 |
+
|
| 130 |
+
## Intended use
|
| 131 |
+
|
| 132 |
+
This adapter is intended for **research purposes only** as part of the
|
| 133 |
+
[AIML589 project](https://github.com/1jamesthompson1/AIML589), which
|
| 134 |
+
investigates value alignment of LLMs with New Zealand population
|
| 135 |
+
distributions from the World Values Survey.
|
| 136 |
+
|
| 137 |
+
### Out-of-scope
|
| 138 |
+
|
| 139 |
+
This model has not been safety-tuned for general-purpose deployment.
|
| 140 |
+
It should not be used in production systems, for making decisions about
|
| 141 |
+
people, or in contexts where reliability and safety are critical.
|
| 142 |
+
|
| 143 |
+
## Limitations and biases
|
| 144 |
+
|
| 145 |
+
- Fine-tuned on a single WVS wave (Wave 7) for New Zealand only.
|
| 146 |
+
- The training data reflects the values of those who responded to the
|
| 147 |
+
survey and may not represent all New Zealanders.
|
| 148 |
+
- LoRA adapters are subject to the limitations and biases of the base
|
| 149 |
+
model ([Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B)).
|
adapter_config.json
ADDED
|
@@ -0,0 +1,50 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"alora_invocation_tokens": null,
|
| 3 |
+
"alpha_pattern": {},
|
| 4 |
+
"arrow_config": null,
|
| 5 |
+
"auto_mapping": null,
|
| 6 |
+
"base_model_name_or_path": "Qwen/Qwen3.5-9B",
|
| 7 |
+
"bias": "none",
|
| 8 |
+
"corda_config": null,
|
| 9 |
+
"ensure_weight_tying": false,
|
| 10 |
+
"eva_config": null,
|
| 11 |
+
"exclude_modules": null,
|
| 12 |
+
"fan_in_fan_out": false,
|
| 13 |
+
"inference_mode": true,
|
| 14 |
+
"init_lora_weights": true,
|
| 15 |
+
"layer_replication": null,
|
| 16 |
+
"layers_pattern": null,
|
| 17 |
+
"layers_to_transform": null,
|
| 18 |
+
"loftq_config": {},
|
| 19 |
+
"lora_alpha": 32,
|
| 20 |
+
"lora_bias": false,
|
| 21 |
+
"lora_dropout": 0.05,
|
| 22 |
+
"lora_ga_config": null,
|
| 23 |
+
"megatron_config": null,
|
| 24 |
+
"megatron_core": "megatron.core",
|
| 25 |
+
"modules_to_save": null,
|
| 26 |
+
"monteclora_config": null,
|
| 27 |
+
"peft_type": "LORA",
|
| 28 |
+
"peft_version": "0.20.0",
|
| 29 |
+
"qalora_group_size": 16,
|
| 30 |
+
"r": 16,
|
| 31 |
+
"rank_pattern": {},
|
| 32 |
+
"revision": null,
|
| 33 |
+
"target_modules": [
|
| 34 |
+
"down_proj",
|
| 35 |
+
"o_proj",
|
| 36 |
+
"v_proj",
|
| 37 |
+
"q_proj",
|
| 38 |
+
"up_proj",
|
| 39 |
+
"gate_proj",
|
| 40 |
+
"k_proj"
|
| 41 |
+
],
|
| 42 |
+
"target_parameters": null,
|
| 43 |
+
"task_type": "CAUSAL_LM",
|
| 44 |
+
"trainable_token_indices": null,
|
| 45 |
+
"use_bdlora": null,
|
| 46 |
+
"use_dora": false,
|
| 47 |
+
"use_qalora": false,
|
| 48 |
+
"use_rslora": false,
|
| 49 |
+
"velora_config": null
|
| 50 |
+
}
|
adapter_model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:60bbbe955503eda1da31b438dd00af3674448236c1dc1944aba4e0fe2be0ab9f
|
| 3 |
+
size 116425880
|
chat_template.jinja
ADDED
|
@@ -0,0 +1,154 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{%- set image_count = namespace(value=0) %}
|
| 2 |
+
{%- set video_count = namespace(value=0) %}
|
| 3 |
+
{%- macro render_content(content, do_vision_count, is_system_content=false) %}
|
| 4 |
+
{%- if content is string %}
|
| 5 |
+
{{- content }}
|
| 6 |
+
{%- elif content is iterable and content is not mapping %}
|
| 7 |
+
{%- for item in content %}
|
| 8 |
+
{%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
|
| 9 |
+
{%- if is_system_content %}
|
| 10 |
+
{{- raise_exception('System message cannot contain images.') }}
|
| 11 |
+
{%- endif %}
|
| 12 |
+
{%- if do_vision_count %}
|
| 13 |
+
{%- set image_count.value = image_count.value + 1 %}
|
| 14 |
+
{%- endif %}
|
| 15 |
+
{%- if add_vision_id %}
|
| 16 |
+
{{- 'Picture ' ~ image_count.value ~ ': ' }}
|
| 17 |
+
{%- endif %}
|
| 18 |
+
{{- '<|vision_start|><|image_pad|><|vision_end|>' }}
|
| 19 |
+
{%- elif 'video' in item or item.type == 'video' %}
|
| 20 |
+
{%- if is_system_content %}
|
| 21 |
+
{{- raise_exception('System message cannot contain videos.') }}
|
| 22 |
+
{%- endif %}
|
| 23 |
+
{%- if do_vision_count %}
|
| 24 |
+
{%- set video_count.value = video_count.value + 1 %}
|
| 25 |
+
{%- endif %}
|
| 26 |
+
{%- if add_vision_id %}
|
| 27 |
+
{{- 'Video ' ~ video_count.value ~ ': ' }}
|
| 28 |
+
{%- endif %}
|
| 29 |
+
{{- '<|vision_start|><|video_pad|><|vision_end|>' }}
|
| 30 |
+
{%- elif 'text' in item %}
|
| 31 |
+
{{- item.text }}
|
| 32 |
+
{%- else %}
|
| 33 |
+
{{- raise_exception('Unexpected item type in content.') }}
|
| 34 |
+
{%- endif %}
|
| 35 |
+
{%- endfor %}
|
| 36 |
+
{%- elif content is none or content is undefined %}
|
| 37 |
+
{{- '' }}
|
| 38 |
+
{%- else %}
|
| 39 |
+
{{- raise_exception('Unexpected content type.') }}
|
| 40 |
+
{%- endif %}
|
| 41 |
+
{%- endmacro %}
|
| 42 |
+
{%- if not messages %}
|
| 43 |
+
{{- raise_exception('No messages provided.') }}
|
| 44 |
+
{%- endif %}
|
| 45 |
+
{%- if tools and tools is iterable and tools is not mapping %}
|
| 46 |
+
{{- '<|im_start|>system\n' }}
|
| 47 |
+
{{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
|
| 48 |
+
{%- for tool in tools %}
|
| 49 |
+
{{- "\n" }}
|
| 50 |
+
{{- tool | tojson }}
|
| 51 |
+
{%- endfor %}
|
| 52 |
+
{{- "\n</tools>" }}
|
| 53 |
+
{{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
|
| 54 |
+
{%- if messages[0].role == 'system' %}
|
| 55 |
+
{%- set content = render_content(messages[0].content, false, true)|trim %}
|
| 56 |
+
{%- if content %}
|
| 57 |
+
{{- '\n\n' + content }}
|
| 58 |
+
{%- endif %}
|
| 59 |
+
{%- endif %}
|
| 60 |
+
{{- '<|im_end|>\n' }}
|
| 61 |
+
{%- else %}
|
| 62 |
+
{%- if messages[0].role == 'system' %}
|
| 63 |
+
{%- set content = render_content(messages[0].content, false, true)|trim %}
|
| 64 |
+
{{- '<|im_start|>system\n' + content + '<|im_end|>\n' }}
|
| 65 |
+
{%- endif %}
|
| 66 |
+
{%- endif %}
|
| 67 |
+
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
| 68 |
+
{%- for message in messages[::-1] %}
|
| 69 |
+
{%- set index = (messages|length - 1) - loop.index0 %}
|
| 70 |
+
{%- if ns.multi_step_tool and message.role == "user" %}
|
| 71 |
+
{%- set content = render_content(message.content, false)|trim %}
|
| 72 |
+
{%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
|
| 73 |
+
{%- set ns.multi_step_tool = false %}
|
| 74 |
+
{%- set ns.last_query_index = index %}
|
| 75 |
+
{%- endif %}
|
| 76 |
+
{%- endif %}
|
| 77 |
+
{%- endfor %}
|
| 78 |
+
{%- if ns.multi_step_tool %}
|
| 79 |
+
{{- raise_exception('No user query found in messages.') }}
|
| 80 |
+
{%- endif %}
|
| 81 |
+
{%- for message in messages %}
|
| 82 |
+
{%- set content = render_content(message.content, true)|trim %}
|
| 83 |
+
{%- if message.role == "system" %}
|
| 84 |
+
{%- if not loop.first %}
|
| 85 |
+
{{- raise_exception('System message must be at the beginning.') }}
|
| 86 |
+
{%- endif %}
|
| 87 |
+
{%- elif message.role == "user" %}
|
| 88 |
+
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
|
| 89 |
+
{%- elif message.role == "assistant" %}
|
| 90 |
+
{%- set reasoning_content = '' %}
|
| 91 |
+
{%- if message.reasoning_content is string %}
|
| 92 |
+
{%- set reasoning_content = message.reasoning_content %}
|
| 93 |
+
{%- else %}
|
| 94 |
+
{%- if '</think>' in content %}
|
| 95 |
+
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
| 96 |
+
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
|
| 97 |
+
{%- endif %}
|
| 98 |
+
{%- endif %}
|
| 99 |
+
{%- set reasoning_content = reasoning_content|trim %}
|
| 100 |
+
{%- if loop.index0 > ns.last_query_index %}
|
| 101 |
+
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
|
| 102 |
+
{%- else %}
|
| 103 |
+
{{- '<|im_start|>' + message.role + '\n' + content }}
|
| 104 |
+
{%- endif %}
|
| 105 |
+
{%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
|
| 106 |
+
{%- for tool_call in message.tool_calls %}
|
| 107 |
+
{%- if tool_call.function is defined %}
|
| 108 |
+
{%- set tool_call = tool_call.function %}
|
| 109 |
+
{%- endif %}
|
| 110 |
+
{%- if loop.first %}
|
| 111 |
+
{%- if content|trim %}
|
| 112 |
+
{{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
|
| 113 |
+
{%- else %}
|
| 114 |
+
{{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
|
| 115 |
+
{%- endif %}
|
| 116 |
+
{%- else %}
|
| 117 |
+
{{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
|
| 118 |
+
{%- endif %}
|
| 119 |
+
{%- if tool_call.arguments is defined %}
|
| 120 |
+
{%- for args_name, args_value in tool_call.arguments|items %}
|
| 121 |
+
{{- '<parameter=' + args_name + '>\n' }}
|
| 122 |
+
{%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
|
| 123 |
+
{{- args_value }}
|
| 124 |
+
{{- '\n</parameter>\n' }}
|
| 125 |
+
{%- endfor %}
|
| 126 |
+
{%- endif %}
|
| 127 |
+
{{- '</function>\n</tool_call>' }}
|
| 128 |
+
{%- endfor %}
|
| 129 |
+
{%- endif %}
|
| 130 |
+
{{- '<|im_end|>\n' }}
|
| 131 |
+
{%- elif message.role == "tool" %}
|
| 132 |
+
{%- if loop.previtem and loop.previtem.role != "tool" %}
|
| 133 |
+
{{- '<|im_start|>user' }}
|
| 134 |
+
{%- endif %}
|
| 135 |
+
{{- '\n<tool_response>\n' }}
|
| 136 |
+
{{- content }}
|
| 137 |
+
{{- '\n</tool_response>' }}
|
| 138 |
+
{%- if not loop.last and loop.nextitem.role != "tool" %}
|
| 139 |
+
{{- '<|im_end|>\n' }}
|
| 140 |
+
{%- elif loop.last %}
|
| 141 |
+
{{- '<|im_end|>\n' }}
|
| 142 |
+
{%- endif %}
|
| 143 |
+
{%- else %}
|
| 144 |
+
{{- raise_exception('Unexpected message role.') }}
|
| 145 |
+
{%- endif %}
|
| 146 |
+
{%- endfor %}
|
| 147 |
+
{%- if add_generation_prompt %}
|
| 148 |
+
{{- '<|im_start|>assistant\n' }}
|
| 149 |
+
{%- if enable_thinking is defined and enable_thinking is false %}
|
| 150 |
+
{{- '<think>\n\n</think>\n\n' }}
|
| 151 |
+
{%- else %}
|
| 152 |
+
{{- '<think>\n' }}
|
| 153 |
+
{%- endif %}
|
| 154 |
+
{%- endif %}
|
environment.json
ADDED
|
@@ -0,0 +1,82 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"Jinja2": "3.1.6",
|
| 3 |
+
"MarkupSafe": "3.0.3",
|
| 4 |
+
"PyYAML": "6.0.3",
|
| 5 |
+
"Pygments": "2.20.0",
|
| 6 |
+
"accelerate": "1.14.0",
|
| 7 |
+
"aiohappyeyeballs": "2.7.1",
|
| 8 |
+
"aiohttp": "3.14.3",
|
| 9 |
+
"aiosignal": "1.4.0",
|
| 10 |
+
"annotated-doc": "0.0.5",
|
| 11 |
+
"anyio": "4.14.2",
|
| 12 |
+
"attrs": "26.1.0",
|
| 13 |
+
"bitsandbytes": "0.50.0",
|
| 14 |
+
"certifi": "2026.7.22",
|
| 15 |
+
"charset-normalizer": "3.4.9",
|
| 16 |
+
"click": "8.4.2",
|
| 17 |
+
"cuda-bindings": "13.3.1",
|
| 18 |
+
"cuda-pathfinder": "1.6.0",
|
| 19 |
+
"cuda-toolkit": "13.0.3.0",
|
| 20 |
+
"datasets": "5.0.1",
|
| 21 |
+
"dill": "0.4.1",
|
| 22 |
+
"filelock": "3.32.0",
|
| 23 |
+
"frozenlist": "1.8.0",
|
| 24 |
+
"fsspec": "2026.6.0",
|
| 25 |
+
"h11": "0.16.0",
|
| 26 |
+
"hf-xet": "1.5.2",
|
| 27 |
+
"httpcore": "1.0.9",
|
| 28 |
+
"httpx": "0.28.1",
|
| 29 |
+
"huggingface_hub": "1.25.1",
|
| 30 |
+
"idna": "3.18",
|
| 31 |
+
"markdown-it-py": "4.2.0",
|
| 32 |
+
"mdurl": "0.1.2",
|
| 33 |
+
"mpmath": "1.3.0",
|
| 34 |
+
"multidict": "6.7.1",
|
| 35 |
+
"multiprocess": "0.70.19",
|
| 36 |
+
"networkx": "3.6.1",
|
| 37 |
+
"numpy": "2.5.1",
|
| 38 |
+
"nvidia-cublas": "13.1.1.3",
|
| 39 |
+
"nvidia-cuda-cupti": "13.0.85",
|
| 40 |
+
"nvidia-cuda-nvrtc": "13.0.88",
|
| 41 |
+
"nvidia-cuda-runtime": "13.0.96",
|
| 42 |
+
"nvidia-cudnn-cu13": "9.20.0.48",
|
| 43 |
+
"nvidia-cufft": "12.0.0.61",
|
| 44 |
+
"nvidia-cufile": "1.15.1.6",
|
| 45 |
+
"nvidia-curand": "10.4.0.35",
|
| 46 |
+
"nvidia-cusolver": "12.0.4.66",
|
| 47 |
+
"nvidia-cusparse": "12.6.3.3",
|
| 48 |
+
"nvidia-cusparselt-cu13": "0.8.1",
|
| 49 |
+
"nvidia-nccl-cu13": "2.29.7",
|
| 50 |
+
"nvidia-nvjitlink": "13.3.33",
|
| 51 |
+
"nvidia-nvshmem-cu13": "3.4.5",
|
| 52 |
+
"nvidia-nvtx": "13.0.85",
|
| 53 |
+
"packaging": "26.2",
|
| 54 |
+
"pandas": "3.0.5",
|
| 55 |
+
"peft": "0.20.0",
|
| 56 |
+
"pillow": "12.3.0",
|
| 57 |
+
"propcache": "0.5.2",
|
| 58 |
+
"psutil": "7.2.2",
|
| 59 |
+
"pyarrow": "25.0.0",
|
| 60 |
+
"python-dateutil": "2.9.0.post0",
|
| 61 |
+
"python-dotenv": "1.2.2",
|
| 62 |
+
"regex": "2026.7.19",
|
| 63 |
+
"requests": "2.34.2",
|
| 64 |
+
"rich": "15.0.0",
|
| 65 |
+
"safetensors": "0.8.0",
|
| 66 |
+
"setuptools": "83.0.0",
|
| 67 |
+
"shellingham": "1.5.4",
|
| 68 |
+
"six": "1.17.0",
|
| 69 |
+
"sympy": "1.14.0",
|
| 70 |
+
"tokenizers": "0.22.2",
|
| 71 |
+
"torch": "2.13.0",
|
| 72 |
+
"torchvision": "0.28.0",
|
| 73 |
+
"tqdm": "4.70.0",
|
| 74 |
+
"transformers": "5.14.1",
|
| 75 |
+
"triton": "3.7.1",
|
| 76 |
+
"trl": "1.9.2",
|
| 77 |
+
"typer": "0.27.0",
|
| 78 |
+
"typing_extensions": "4.16.0",
|
| 79 |
+
"urllib3": "2.7.0",
|
| 80 |
+
"xxhash": "3.8.1",
|
| 81 |
+
"yarl": "1.24.5"
|
| 82 |
+
}
|
finetune_config.json
ADDED
|
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"model": "Qwen/Qwen3.5-9B",
|
| 3 |
+
"model_sha": "c202236235762e1c871ad0ccb60c8ee5ba337b9a",
|
| 4 |
+
"dataset": "sampled_response",
|
| 5 |
+
"dataset_sha": "38f42265e06d60afd0a5d2d45808202e60e95103",
|
| 6 |
+
"subpopulation": "cluster_0",
|
| 7 |
+
"gpu": "NVIDIA RTX 6000 Ada Generation",
|
| 8 |
+
"library_name": "peft",
|
| 9 |
+
"pipeline_tag": "text-generation",
|
| 10 |
+
"timestamp": "2026-08-05T15:57:46.834571",
|
| 11 |
+
"hyperparameters": {
|
| 12 |
+
"lora_r": 16,
|
| 13 |
+
"lora_alpha": 32,
|
| 14 |
+
"lora_dropout": 0.05,
|
| 15 |
+
"dora": false,
|
| 16 |
+
"quantization": null,
|
| 17 |
+
"lr": 0.0002,
|
| 18 |
+
"num_epochs": 3,
|
| 19 |
+
"batch_size": 4,
|
| 20 |
+
"gradient_accumulation_steps": 4,
|
| 21 |
+
"max_seq_length": 1024,
|
| 22 |
+
"warmup_ratio": 0.1,
|
| 23 |
+
"dtype": "bf16"
|
| 24 |
+
}
|
| 25 |
+
}
|
processor_config.json
ADDED
|
@@ -0,0 +1,60 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"image_processor": {
|
| 3 |
+
"do_convert_rgb": true,
|
| 4 |
+
"do_normalize": true,
|
| 5 |
+
"do_rescale": true,
|
| 6 |
+
"do_resize": true,
|
| 7 |
+
"image_mean": [
|
| 8 |
+
0.5,
|
| 9 |
+
0.5,
|
| 10 |
+
0.5
|
| 11 |
+
],
|
| 12 |
+
"image_processor_type": "Qwen2VLImageProcessor",
|
| 13 |
+
"image_std": [
|
| 14 |
+
0.5,
|
| 15 |
+
0.5,
|
| 16 |
+
0.5
|
| 17 |
+
],
|
| 18 |
+
"merge_size": 2,
|
| 19 |
+
"patch_size": 16,
|
| 20 |
+
"resample": 3,
|
| 21 |
+
"rescale_factor": 0.00392156862745098,
|
| 22 |
+
"size": {
|
| 23 |
+
"longest_edge": 16777216,
|
| 24 |
+
"shortest_edge": 65536
|
| 25 |
+
},
|
| 26 |
+
"temporal_patch_size": 2
|
| 27 |
+
},
|
| 28 |
+
"processor_class": "Qwen3VLProcessor",
|
| 29 |
+
"video_processor": {
|
| 30 |
+
"do_convert_rgb": true,
|
| 31 |
+
"do_normalize": true,
|
| 32 |
+
"do_rescale": true,
|
| 33 |
+
"do_resize": true,
|
| 34 |
+
"do_sample_frames": true,
|
| 35 |
+
"fps": 2,
|
| 36 |
+
"image_mean": [
|
| 37 |
+
0.5,
|
| 38 |
+
0.5,
|
| 39 |
+
0.5
|
| 40 |
+
],
|
| 41 |
+
"image_std": [
|
| 42 |
+
0.5,
|
| 43 |
+
0.5,
|
| 44 |
+
0.5
|
| 45 |
+
],
|
| 46 |
+
"max_frames": 768,
|
| 47 |
+
"merge_size": 2,
|
| 48 |
+
"min_frames": 4,
|
| 49 |
+
"patch_size": 16,
|
| 50 |
+
"resample": 3,
|
| 51 |
+
"rescale_factor": 0.00392156862745098,
|
| 52 |
+
"return_metadata": false,
|
| 53 |
+
"size": {
|
| 54 |
+
"longest_edge": 25165824,
|
| 55 |
+
"shortest_edge": 4096
|
| 56 |
+
},
|
| 57 |
+
"temporal_patch_size": 2,
|
| 58 |
+
"video_processor_type": "Qwen3VLVideoProcessor"
|
| 59 |
+
}
|
| 60 |
+
}
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
|
| 3 |
+
size 19989325
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,32 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"audio_bos_token": "<|audio_start|>",
|
| 4 |
+
"audio_eos_token": "<|audio_end|>",
|
| 5 |
+
"audio_token": "<|audio_pad|>",
|
| 6 |
+
"backend": "tokenizers",
|
| 7 |
+
"bos_token": null,
|
| 8 |
+
"clean_up_tokenization_spaces": false,
|
| 9 |
+
"eos_token": "<|im_end|>",
|
| 10 |
+
"errors": "replace",
|
| 11 |
+
"image_token": "<|image_pad|>",
|
| 12 |
+
"is_local": false,
|
| 13 |
+
"local_files_only": false,
|
| 14 |
+
"model_max_length": 262144,
|
| 15 |
+
"model_specific_special_tokens": {
|
| 16 |
+
"audio_bos_token": "<|audio_start|>",
|
| 17 |
+
"audio_eos_token": "<|audio_end|>",
|
| 18 |
+
"audio_token": "<|audio_pad|>",
|
| 19 |
+
"image_token": "<|image_pad|>",
|
| 20 |
+
"video_token": "<|video_pad|>",
|
| 21 |
+
"vision_bos_token": "<|vision_start|>",
|
| 22 |
+
"vision_eos_token": "<|vision_end|>"
|
| 23 |
+
},
|
| 24 |
+
"pad_token": "<|endoftext|>",
|
| 25 |
+
"pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
|
| 26 |
+
"split_special_tokens": false,
|
| 27 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 28 |
+
"unk_token": null,
|
| 29 |
+
"video_token": "<|video_pad|>",
|
| 30 |
+
"vision_bos_token": "<|vision_start|>",
|
| 31 |
+
"vision_eos_token": "<|vision_end|>"
|
| 32 |
+
}
|
training.log
ADDED
|
@@ -0,0 +1,16 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
logging to output/sampled_response/cluster_0/training.log
|
| 2 |
+
[sft] loading tokenizer...
|
| 3 |
+
[sft] loading model...
|
| 4 |
+
[sft] tokenizing 1206 examples...
|
| 5 |
+
[sft] tokenizing validation split for eval...
|
| 6 |
+
[data] loaded 150 validation examples (config=sampled_response, subpop=cluster_0)
|
| 7 |
+
[sft] eval split: 150 examples
|
| 8 |
+
[sft] starting training...
|
| 9 |
+
[sft] saving...
|
| 10 |
+
saved to output/sampled_response/cluster_0
|
| 11 |
+
gpu: NVIDIA RTX 6000 Ada Generation time: 1103s
|
| 12 |
+
training log -> output/sampled_response/cluster_0/training_log.json
|
| 13 |
+
run metadata -> output/sampled_response/cluster_0/finetune_config.json
|
| 14 |
+
environment -> output/sampled_response/cluster_0/environment.json
|
| 15 |
+
README -> output/sampled_response/cluster_0/README.md
|
| 16 |
+
[upload] uploading output/sampled_response/cluster_0 to https://huggingface.co/1jamesthompson1/Qwen3.5-9B-nz-wvs-sampled_response-cluster_0...
|
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
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|
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|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6c72d5679628010873e66334a077e6049a84120b54a54c16156c4987e4e2628e
|
| 3 |
+
size 5713
|
training_log.json
ADDED
|
@@ -0,0 +1,484 @@
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|
| 1 |
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[
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| 2 |
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| 3 |
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