Instructions to use 17Lab/qwen7b-lora-sft-s13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use 17Lab/qwen7b-lora-sft-s13 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B") model = PeftModel.from_pretrained(base_model, "17Lab/qwen7b-lora-sft-s13") - Transformers
How to use 17Lab/qwen7b-lora-sft-s13 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="17Lab/qwen7b-lora-sft-s13") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("17Lab/qwen7b-lora-sft-s13", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use 17Lab/qwen7b-lora-sft-s13 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "17Lab/qwen7b-lora-sft-s13" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "17Lab/qwen7b-lora-sft-s13", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/17Lab/qwen7b-lora-sft-s13
- SGLang
How to use 17Lab/qwen7b-lora-sft-s13 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 "17Lab/qwen7b-lora-sft-s13" \ --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": "17Lab/qwen7b-lora-sft-s13", "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 "17Lab/qwen7b-lora-sft-s13" \ --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": "17Lab/qwen7b-lora-sft-s13", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use 17Lab/qwen7b-lora-sft-s13 with Docker Model Runner:
docker model run hf.co/17Lab/qwen7b-lora-sft-s13
Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +72 -0
- adapter_config.json +46 -0
- adapter_model.safetensors +3 -0
- all_results.json +12 -0
- chat_template.jinja +54 -0
- eval_results.json +7 -0
- tokenizer.json +3 -0
- tokenizer_config.json +31 -0
- train_results.json +8 -0
- trainer_log.jsonl +51 -0
- trainer_state.json +398 -0
- training_args.bin +3 -0
.gitattributes
CHANGED
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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| 1 |
+
---
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| 2 |
+
library_name: peft
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| 3 |
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license: other
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| 4 |
+
base_model: Qwen/Qwen2.5-7B
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| 5 |
+
tags:
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| 6 |
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- base_model:adapter:Qwen/Qwen2.5-7B
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| 7 |
+
- llama-factory
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| 8 |
+
- lora
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| 9 |
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- transformers
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| 10 |
+
pipeline_tag: text-generation
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| 11 |
+
model-index:
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| 12 |
+
- name: qwen7b_lora_sft_8gpu_s13
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| 13 |
+
results: []
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| 14 |
+
---
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| 15 |
+
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| 16 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 17 |
+
should probably proofread and complete it, then remove this comment. -->
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| 18 |
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| 19 |
+
# qwen7b_lora_sft_8gpu_s13
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| 20 |
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This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the assimilation_strict_json_v2 dataset.
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| 22 |
+
It achieves the following results on the evaluation set:
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| 23 |
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- Loss: 0.1527
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| 24 |
+
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| 25 |
+
## Model description
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More information needed
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## Intended uses & limitations
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+
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More information needed
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| 32 |
+
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## Training and evaluation data
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| 34 |
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More information needed
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| 36 |
+
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| 37 |
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## Training procedure
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| 38 |
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| 39 |
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### Training hyperparameters
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| 40 |
+
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| 41 |
+
The following hyperparameters were used during training:
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| 42 |
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- learning_rate: 0.0001
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| 43 |
+
- train_batch_size: 8
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| 44 |
+
- eval_batch_size: 4
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| 45 |
+
- seed: 13
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| 46 |
+
- distributed_type: multi-GPU
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| 47 |
+
- num_devices: 8
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| 48 |
+
- total_train_batch_size: 64
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| 49 |
+
- total_eval_batch_size: 32
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| 50 |
+
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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| 51 |
+
- lr_scheduler_type: cosine
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| 52 |
+
- lr_scheduler_warmup_steps: 0.05
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| 53 |
+
- num_epochs: 3
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| 54 |
+
|
| 55 |
+
### Training results
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| 56 |
+
|
| 57 |
+
| Training Loss | Epoch | Step | Validation Loss |
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| 58 |
+
|:-------------:|:------:|:----:|:---------------:|
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| 59 |
+
| 0.1567 | 0.6667 | 50 | 0.1561 |
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| 60 |
+
| 0.1533 | 1.3333 | 100 | 0.1532 |
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| 61 |
+
| 0.1528 | 2.0 | 150 | 0.1527 |
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| 62 |
+
| 0.1527 | 2.6667 | 200 | 0.1527 |
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| 63 |
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| 0.1527 | 3.0 | 225 | 0.1527 |
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| 64 |
+
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| 65 |
+
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| 66 |
+
### Framework versions
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| 67 |
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| 68 |
+
- PEFT 0.18.1
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| 69 |
+
- Transformers 5.6.0
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| 70 |
+
- Pytorch 2.7.0+cu128
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| 71 |
+
- Datasets 4.0.0
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| 72 |
+
- Tokenizers 0.22.2
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adapter_config.json
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{
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| 2 |
+
"alora_invocation_tokens": null,
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| 3 |
+
"alpha_pattern": {},
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| 4 |
+
"arrow_config": null,
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| 5 |
+
"auto_mapping": null,
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| 6 |
+
"base_model_name_or_path": "Qwen/Qwen2.5-7B",
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| 7 |
+
"bias": "none",
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| 8 |
+
"corda_config": null,
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| 9 |
+
"ensure_weight_tying": false,
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| 10 |
+
"eva_config": null,
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| 11 |
+
"exclude_modules": null,
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| 12 |
+
"fan_in_fan_out": false,
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| 13 |
+
"inference_mode": true,
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| 14 |
+
"init_lora_weights": true,
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| 15 |
+
"layer_replication": null,
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| 16 |
+
"layers_pattern": null,
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| 17 |
+
"layers_to_transform": null,
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| 18 |
+
"loftq_config": {},
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| 19 |
+
"lora_alpha": 128,
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| 20 |
+
"lora_bias": false,
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| 21 |
+
"lora_dropout": 0.05,
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| 22 |
+
"megatron_config": null,
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| 23 |
+
"megatron_core": "megatron.core",
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| 24 |
+
"modules_to_save": null,
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| 25 |
+
"peft_type": "LORA",
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| 26 |
+
"peft_version": "0.18.1",
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| 27 |
+
"qalora_group_size": 16,
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| 28 |
+
"r": 64,
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| 29 |
+
"rank_pattern": {},
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| 30 |
+
"revision": null,
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| 31 |
+
"target_modules": [
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| 32 |
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"up_proj",
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| 33 |
+
"down_proj",
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| 34 |
+
"gate_proj",
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| 35 |
+
"o_proj",
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| 36 |
+
"q_proj",
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| 37 |
+
"v_proj",
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| 38 |
+
"k_proj"
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| 39 |
+
],
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| 40 |
+
"target_parameters": null,
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| 41 |
+
"task_type": "CAUSAL_LM",
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| 42 |
+
"trainable_token_indices": null,
|
| 43 |
+
"use_dora": false,
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| 44 |
+
"use_qalora": false,
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| 45 |
+
"use_rslora": false
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| 46 |
+
}
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adapter_model.safetensors
ADDED
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@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1bd5612db05d87fc488ecab6daff65b402647d54e5463e5e31a299dbc56dce98
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| 3 |
+
size 645975704
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all_results.json
ADDED
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{
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"epoch": 3.0,
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| 3 |
+
"eval_loss": 0.1526603251695633,
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| 4 |
+
"eval_runtime": 0.7925,
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| 5 |
+
"eval_samples_per_second": 315.469,
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| 6 |
+
"eval_steps_per_second": 10.095,
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| 7 |
+
"total_flos": 9.49777223122944e+16,
|
| 8 |
+
"train_loss": 0.29038675096299915,
|
| 9 |
+
"train_runtime": 174.0086,
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| 10 |
+
"train_samples_per_second": 81.892,
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| 11 |
+
"train_steps_per_second": 1.293
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| 12 |
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}
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chat_template.jinja
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| 1 |
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{%- if tools %}
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| 2 |
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{{- '<|im_start|>system\n' }}
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| 3 |
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{%- if messages[0]['role'] == 'system' %}
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| 4 |
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{{- messages[0]['content'] }}
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{%- else %}
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| 6 |
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{{- 'You are a helpful assistant.' }}
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| 7 |
+
{%- endif %}
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| 8 |
+
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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| 9 |
+
{%- for tool in tools %}
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| 10 |
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{{- "\n" }}
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| 11 |
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{{- tool | tojson }}
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+
{%- endfor %}
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| 13 |
+
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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| 14 |
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{%- else %}
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| 15 |
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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| 17 |
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{%- else %}
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| 18 |
+
{{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
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| 19 |
+
{%- endif %}
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| 20 |
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{%- endif %}
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| 21 |
+
{%- for message in messages %}
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| 22 |
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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| 23 |
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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| 24 |
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{%- elif message.role == "assistant" %}
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| 25 |
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{{- '<|im_start|>' + message.role }}
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| 26 |
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{%- if message.content %}
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| 27 |
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{{- '\n' + message.content }}
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| 28 |
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{%- endif %}
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| 29 |
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{%- for tool_call in message.tool_calls %}
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| 30 |
+
{%- if tool_call.function is defined %}
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| 31 |
+
{%- set tool_call = tool_call.function %}
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| 32 |
+
{%- endif %}
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| 33 |
+
{{- '\n<tool_call>\n{"name": "' }}
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| 34 |
+
{{- tool_call.name }}
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| 35 |
+
{{- '", "arguments": ' }}
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| 36 |
+
{{- tool_call.arguments | tojson }}
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| 37 |
+
{{- '}\n</tool_call>' }}
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| 38 |
+
{%- endfor %}
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| 39 |
+
{{- '<|im_end|>\n' }}
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| 40 |
+
{%- elif message.role == "tool" %}
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| 41 |
+
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
| 42 |
+
{{- '<|im_start|>user' }}
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| 43 |
+
{%- endif %}
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| 44 |
+
{{- '\n<tool_response>\n' }}
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| 45 |
+
{{- message.content }}
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| 46 |
+
{{- '\n</tool_response>' }}
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| 47 |
+
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
| 48 |
+
{{- '<|im_end|>\n' }}
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| 49 |
+
{%- endif %}
|
| 50 |
+
{%- endif %}
|
| 51 |
+
{%- endfor %}
|
| 52 |
+
{%- if add_generation_prompt %}
|
| 53 |
+
{{- '<|im_start|>assistant\n' }}
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| 54 |
+
{%- endif %}
|
eval_results.json
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{
|
| 2 |
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"epoch": 3.0,
|
| 3 |
+
"eval_loss": 0.1526603251695633,
|
| 4 |
+
"eval_runtime": 0.7925,
|
| 5 |
+
"eval_samples_per_second": 315.469,
|
| 6 |
+
"eval_steps_per_second": 10.095
|
| 7 |
+
}
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tokenizer.json
ADDED
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version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
|
| 3 |
+
size 11421892
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tokenizer_config.json
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|
| 1 |
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{
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| 2 |
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| 3 |
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| 4 |
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| 5 |
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| 6 |
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| 7 |
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| 8 |
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| 9 |
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| 10 |
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"<|im_end|>",
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| 11 |
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"<|object_ref_start|>",
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| 12 |
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| 13 |
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"<|box_start|>",
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| 14 |
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| 15 |
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"<|quad_start|>",
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| 16 |
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"<|quad_end|>",
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| 17 |
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"<|vision_start|>",
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| 18 |
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| 19 |
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"<|vision_pad|>",
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| 20 |
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| 21 |
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"<|video_pad|>"
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| 22 |
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| 23 |
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| 24 |
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| 25 |
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| 26 |
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| 27 |
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| 28 |
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| 29 |
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|
| 30 |
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| 31 |
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|
train_results.json
ADDED
|
@@ -0,0 +1,8 @@
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| 1 |
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{
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| 7 |
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| 8 |
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|
trainer_log.jsonl
ADDED
|
@@ -0,0 +1,51 @@
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|
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| 1 |
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trainer_state.json
ADDED
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@@ -0,0 +1,398 @@
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